Bruises Can Be Accurately Dated or Aged Based on Color

Abstract

The trample dating tin accept important medicolegal implications in family violence and violence against women cases. However, studies evidence that the medical specialist has 50% accuracy in classifying a bruise by age, mainly due to the variability of the images and the color of the bruise. This inquiry proposes a model, based on deep convolutional neural networks, for bruise dating using only images, past age ranges, ranging from 0–two days to 17–thirty days, and images of healthy skin. A 2140 experimental bruise photograph dataset was constructed, for which a data capture protocol and a preprocessing procedure are proposed. Similarly, twenty classification models were trained with the Inception V3, Resnet50, MobileNet, and MnasNet architectures, where combinations of learning transfer, cross-validation, and information augmentation were used. Numerical experiments show that classification models based on MnasNet have meliorate results, reaching 97.00% precision and sensitivity, and 99.50% specificity, exceeding forty% precision reported in the literature. As well, it was observed that the precision of the model decreases with the age of the bruise.

1 INTRODUCTION

According to the latest report of the World Health System (WHO) [1], domestic violence against women is a global scourge, with a prevalence of physical or sexual abuse confronting women of just over 30% globally and 29.8% in Latin America (see Figure 1). Some victims of violence report these facts to the government, which crave forensic clinical services such as bruise dating to support complaints, since the results of dating, used every bit evidence, can be decisive for justice. Nevertheless, many of these complaints are filed due to lack of testify or error in the dating of bruise, which aggravates the consequences of this scourge.

Details are in the caption following the image — Bar graph showing charge per unit of physical and/or sexual violence against women past region, according to the Earth Health System, every bit of 2022 [Color figure can be viewed at wileyonlinelibrary.com]

Bruise, also known as ecchymosis, is the internal bleeding of the skin due to the rupture of blood vessels, which is caused past an impact with an blunt object, without tearing or cutting the skin. Therefore, blood escapes from blood vessels shut to the surface of the skin being trapped under it [two]. This generates a coloration on the surface of the skin, which shows a serial of colors that vary over time and can be visible for up to xxx days from its appearance, and then the medical literature reports the utilize of color scales for dating of an trample. This process consists in determining the age of this trample; thus, the trample is visualized in situ or through photographs, and the judgment of an expert is applied [3], who, in general, is a coroner, and its importance lies in being evidence primal in a trial for domestic violence or against women.

There are studies on the dating of bruise, from the medical and forensic signal of view, where the use of histological assay [4], genetic [5], chromatography [half-dozen], and visual inspection techniques are used, and likewise consider variables such equally sex, age, and skin color of the person. Notwithstanding, due to the variability of the evolution of these bruises [two], there is nonetheless no reliable method to make up one's mind their historic period. This is mainly explained by biological variability, like location, size, depth, and degree of the injury, equally well equally race of the field of study. Also, the biological status, such as diabetes, hemophilia, and leukemia, could affect the advent and healing of bruises. Likewise, studies in the The states and Europe mainly include white-skinned people in their experiments [7-nine], which constitutes a different reality from other regions such as Latin America, where miscegenation is feature.

An exhaustive search in Web of Science, Scopus, and Google Scholar shows that to appointment there are no publications on bruise dating that use information science techniques. Withal, there are artificial intelligence techniques that allow y'all to procedure images and differentiate for nomenclature purposes. Publications prove that the deep learning technique of artificial intelligence allows an accurateness of 90.xvi% for the diagnosis of glaucoma [10], 82.95% for psoriasis [11], and 82.3% for lung diseases [12]. In addition, in [13], information technology is applied to the diagnosis of melanoma, which affects the skin and presents variability in colour, similar trample.

In this work, a deep learning model for bruise dating, based exclusively on images and convolutional neural networks, is proposed, for use on healthy living human being beings only. MnasNet gave better results than the other three architectures evaluated for accurateness. In improver, information technology is optimized for utilize on mobile devices, so it must exist small and fast, to allow a balance between accuracy and latency. To validate the model, the Tensorflow, Keras, and OpenCV libraries were used; so, tests were made with a dataset of 2140 images. Likewise, a protocol is proposed for capturing bruise photographs to guarantee image quality and loftier precision in the results.

This piece of work is organized in v sections. In department two, a review of the literature on bruise dating is made. The bruise dating model and the photograph capture protocol are described in department three. The validation of the model, through the implementation of a system because six classes by age ranges and the "Healthy skin" class, is presented in section four. Finally, the conclusions, limitations, and recommendations are presented in section v.

2 RELATED WORKS

In that location are few works on bruise dating, and these focus on the fields of medicine, biology, and genetics. In forensic medicine, for example, in [6], the use of tristimulus colorimetric is proposed every bit a method to objectively make up one's mind the color of an trample in dark-skinned people using the CIELAB color space, which reaches 95% accurateness of the color of bruise and that could be used for dating. In [15], tristimulus colorimetry is shown to be reliable for the evaluation of the color of a trample generated experimentally with paintballs fired past compressed air guns. The use of a bilirubin meter is evaluated as a bruise dating method in [xvi], where it is found that the departure in bilirubin level between good for you skin and bruise has a summit between mean solar day iv and 5, which decreases in the following days. An alternate low-cal source, in the visible and ultraviolet spectrum, is used in [17] to evaluate its effectiveness in the detection of trample, compared to white calorie-free. Detection is a previous step to trample dating. In medicine and biology, the method of histological assay is used for the dating of trample; however, in [4], it is shown that it is not reliable due to the high variability of the response of human tissue to trauma due to stroke. In the field of genetics, in [5], the apply of genetic expression signatures is proposed as a method to make up one's mind the force of the touch on and the historic period of a bruise in pigs, where differences of +/- two hours are obtained for ages ranging from one to 10 hours, and, due to its physiological and immunological similarity to human peel, it is suggested to extrapolate the results of the study to humans.

An exhaustive review to January 2020, in Web of Science, Scopus, and Google Scholar, based on the use of "bruise dating" search strings, shows that there are no bruise dating works through computational techniques such as artificial intelligence and prototype processing. However, in that location are works of prototype processing and artificial intelligence that have been adult for judge problems to the dating of bruise. The "Relative Attribute SVM + Learning" algorithm is proposed in [18] for the estimation of age based on photographs of human being faces; thus, it is considered that the presence of certain facial attributes at different ages keeps a relative club between age-groups. In [19], it is sought to determine the historic period and gender of a person, and for this, panoramic dental X-ray images are analyzed using image processing and a multilayer perceptron neural network. In another case, [20] proposes a deep hybrid model for nomenclature by age range for man face images, where deep convolutional neural networks are used. The use of a Deep Conventionalities Network, based on rough set theory for the classification of medical images of lung scans, is proposed in [12]. A new algorithm called "Ensemble Margin Instance Pick" (EMIS), based on Random Forest, is proposed in [21], to select the most informative data to optimize the classification of white blood cells. Finally, [22] proposes the use of a convolutional neural network to detect the gender (male or female) of a person based on a photograph of their eyes taken with the front camera of a smartphone, in everyday conditions with a normal camera. For the above, prototype processing and bogus intelligence could be used for bruise dating.

To obtain better results, the epitome preprocessing process, which involves the segmentation of the area of involvement, is included in the paradigm processing methods. [23] proposes using a deep convolutional neural network (DCNN) for the separation of the front and the lesser of an image, with a mean foursquare mistake of three.53%. [10] proposes an approach to the automatic diagnosis of glaucoma called "Super pixels for semi-supervised segmentation" (SP3S) using segmentation, with an F-score of 86.43%. [11] uses a deep convolutional neural network for the segmentation of peel psoriasis biopsy images, differentiating the dermis, epidermis, and non-tissue regions, where 89% accuracy is achieved.

In relation to the bruise dating, the medical literature reports the use of temporal scales based on the coloring of the bruise to guess its age. One of the pioneering scales is that of camps, which establishes a scale of levels, where the color of the trample is ruby-red immediately after being inflicted, then it becomes dark imperial or black, it turns light-green betwixt the fourth and fifth day, yellowish between twenty-four hour period vii to 10, and disappears after fourteen or 15 days. From in that location, various color scales have been established for bruise dating. A literature review on bruise dating scales until 1991 is performed in [two]. Table 1 shows 4 color scales for bruise dating, widely used in the literature. The scales are similar in terms of the sequence of changes in the color of the trample, but differ in the times of these changes, although they all end with the greenish, then yellow color.

Table 1. Bruise dating scales and coloration. Adjusted: [ 2 ]

Source	Bruise color	Bruise historic period
Camps	Red	Immediately
	Dark purple/black	Shortly after
	Green	4 to 5 d
	Yellow	vii to 10 d
	Disappearance	xiv to fifteen d
Glaister	Violet	Immediately
	Blue	twenty-four hours 3
	Greenish	5 to vii d
	Yellow	8 to x d
	Disappearance	13 to 18 d
Polson and Gee	Dark crimson /ruby and black	less than 24 h
	Light-green	day 7
	Yellow	day 14
	Disappearance	up to 30 d
Smith and Fides	Red	Immediately
	Purple/black	Shortly after
	Light-green	4 to 5 d
	Yellow	7 to x d, just pocket-sized and superficial on day iii
	Disappearance	fourteen to 15 d

3 Trample DATING MODEL

A trample dating model using deep learning is proposed, which allows the historic period of a bruise to exist adamant based on a photographic image of it, in living human beings. Its purpose is to determine the age of a bruise in a more than accurate, objective, and quicker way, compared to the dating of bruise made by a human being specialist (coroner and dermatologist). The main components are the protocol for image capture, image preprocessing, and the trained classification model based on convolutional neural networks.

In Figure 2, the bruise dating model receives every bit input a photograph of a bruise that is obtained through a camera respecting a protocol. The prototype is then preprocessed to obtain a clean and segmented epitome of the bruise. This is then processed by the classifier that implements a previously trained convolutional network model, with which the estimated age of the bruise is adamant, this being the outcome of the model.

The utilize of convolutional neural networks is justified because the dating of bruise has low accurateness rates, thus reaching xl% for bruises less than 48 hours, a percent that decreases as the age of the bruise increases [7]. In addition, they present skilful results, comparable to medical specialists, for like bug such as sarcoma [24] and melanoma [xiii] (they affect the peel and its diagnosis is visual based on images).

Therefore, the objective of this study is to build a bruise dating model that manages to exceed the accuracy reported in the literature.

3.1 Protocol for image capture

In guild for the trample dating model to estimate the correct age of the bruise, the photographs must be captured following a series of steps divers under specific atmospheric condition to guarantee the quality of the bruise photographs, which found the main and only characteristic used in this study. Table 2 describes the protocol for bruise image capture.

TABLE 2. Bruise paradigm capture protocol

ID	Pace
P01	Illuminate the environment properly, using either natural or white light
P02	Plough off the photographic camera flash
P03	Set the camera at maximum resolution. The minimum resolution is 1024 × 768 pixels
P04	Inquire the person to discover the peel in the injured surface area, so the skin is gratis of clothing, jewelry, accessories, or other objects
P05	Inquire the person to make clean the injured area, to remove substances such as sand, soil or blood
P06	Hold the camera with both hands perpendicular to the trample, in vertical or horizontal mode
P07	Place the photographic camera at a distance between thirty and 35 centimeters from the bruise
P08	Focus the image so that the bruise is in the center
P09	Verify that no shadows are bandage on the area to exist photographed
P10	Capture 1 photo for each bruise injury. Verify the images are not blurred, shadowed, out of focus, too close or also far
P11	Verify that the photographs have been captured correctly and stored in the camera's retention
P12	Transfer the photographs from the photographic camera to a folder on the calculator where information technology will exist processed past the trample dating model
P13	Avoid reducing the size of the files or losing the resolution of the images during the transfer of the files

3.2 Data preprocessing

The input, both for the nomenclature process and for the learning process, is the photographs of the trample captured post-obit the protocol and digitized in a repository. These images are preprocessed using the binarization of the grayscale prototype to segment the bruise, calculate the centroid of the bruise and trim the epitome to a size of 400 × 400 pixels.

The steps in this procedure are as follows:

Convert a copy of the original prototype to grayscale.
Binarize the grayscale image.
Calculate the position (Ten, Y) of the centroid of trample, using the Moments role of the OpenCV library.
Trim a 400 × 400 pixels portion of the original color prototype, with its center located in the centroid of the bruise, calculated in stride 3.
Save the image, obtained in footstep four, as a new file.

In this way, the original photographs are preprocessed, and a square-shaped image is obtained with the bruise centered in it. To exercise this, a script was adult using the Python programming language and the OpenCV library. Figure 3 shows an original and preprocessed photograph, the latter of 400 × 400 pixels, with the centroid of the bruise in the center of the paradigm.

iii.three Learning

The learning model for trample classification past historic period range is based on convolutional neural networks. The input is the photo, captured following the protocol, and the actual age of the injury. The photographs must be previously preprocessed and organized in folders, co-ordinate to the age (in days) of the injury. In improver, the classes to be used must be established (see, e.g., the scales in Tabular array 1), and the images must be grouped into folders according to these classes. In instance the dataset is non balanced, it is suggested to use the information augmentation technique, which will let greater precision [25].

For learning, it is suggested to evaluate some variants of convolutional neural networks, such every bit Inceptionv3 [26], Resnet50 [27], MobileNet [28], and MnasNet [14].

Previously, ten% of the images for each class should be set apart to be used as the "test" dataset, to prevent information leakage, and to avoid using test data during training.

In Figure 4, the experimentation cycle to find the all-time trample dating model is shown. This wheel is repeated equally many times as necessary, with variants such as the use or not of cantankerous-validation and transfer learning. Each bike tin include the execution of training, validation, testing, and analysis of results activities multiple times, until the model with greatest precision for bruise dating is obtained. All generated models are sent to a file server in the cloud. Finally, the trample dating model with highest accuracy is selected.

3.4 Classification

The convolutional neural network model that obtained the highest precision during the learning phase can exist used to classify new photographs of bruise. The model consists of a file that contains the structure, weights, thresholds, and parameters of the network. The nomenclature model can be implemented as an API (Application Programming Interface), to be consumed past a mobile or web application, or embedded in an off-line mobile application, for trample classification.

The input of the nomenclature model is a trample photograph, and the output is a probability distribution that indicates the bruise belongs to one of the established classes.

4 VALIDATION

The validation process of this study consists in conducting numerical experiments. For this, 4 DCNN models were trained with the dataset detailed in department (dataset). And so, the learning models resulting from each neural network were evaluated using the metrics indicated in department (metrics).

The validation was practical to the Peruvian instance, where the levels of violence against women (see Figure 5) accomplish 68.2%, and 31.7% for physical violence [29], a percentage slightly lower than the earth boilerplate that reaches 31.9% (see Figure ane), and the bulk of the population is mestizo, with a skin color that is not blackness or white. In improver, it should be considered that almost research includes but white-skinned people [2, vii-9, xv, 30-32], and there is a study that indicates that xanthous coloration of a bruise is not visible in people with dark skin [6].

4.ane Dataset

This study requires the construction of a dataset large enough to train a neural network and allocate bruises co-ordinate to their age. For this, a controlled experiment was carried out using a bruise generation method, similar the ane used in [15] and [17]. Two paintball matches were held, with a divergence of 30 days between them. The game consists of firing paintballs with compressed air guns. In this scenario, players often get bruises, despite safety measures such as helmets, vests, protectors, and power limits of weapons. In total, 11 volunteers (one participated in both matches) of mixed skin (four women and seven men), between 25 and 68 years, took v daily photographs of bruise post-obit the data capture protocol detailed in section 3.1, at the same time of the solar day, from game day (day i) to the 24-hour interval 30. Only photographs of unprotected areas of the torso (lower, upper limbs, and buttocks) were obtained, making a total of xviii different bruises. Table 3 shows the characteristics of the volunteers, including sex activity, age, peel colour, health conditions, and location of the trample or bruises. It also shows the camera used to obtain the images, the time of the day, and the location where the image was taken. Thus, the images are like a real and heterogeneous situation.

TABLE 3. Description of test subjects

ID	Sex	Age	Skin photo type (Fitzpatrick)	Bruise location	Camera model	Location	Time of the mean solar day
S1	Female	38	Iv	Arm	Samsung J7	Home	Morning time
S1	Female	38	Iv	Buttock	Samsung J7	Home	Morning time
S2	Male	35	Iii	Arm	Samsung J7	Home Piece of work	Morning
S2	Male	35	Iii	Leg	Samsung J7	Home Piece of work	Morning
S3	Female	31	IV	Arm	Huawei Y6	Home	Night
S4	Male	68	Four	Arm	Samsung J7	Dwelling	Night
S4	Male	68	Four	Back	Samsung J7	Dwelling	Night
S5	Female	28	Iii	Leg	Samsung J1	Home	Afternoon
S6	Female	40	IV	Breast	Huawei P30	Home	Nighttime
S7	Male	37	IV	Arm	Samsung A70	Work	Night
S8	Male person	22	3	Arm	LG K50	Work	Afternoon
S9	Male	40	Iv	Arm	Huawei P8	Habitation	Afternoon
S9	Male	40	Iv	Leg	Huawei P8	Habitation	Afternoon
S10	Male person	22	Four	Arm	Huawei Y5	Work	Afternoon
S11	Male	24	4	Arm	ZTE Blade A602	Home	Afternoon

This way, over a period of 60 days, bruise photographs were collected and a dataset was built with the characteristics detailed in Tabular array 4, which includes the number of images that were used for grooming, validation, and testing of the model, for each of the classes used by forensic doctors in Peru [33]. The dataset is bachelor on request.

TABLE 4. Dataset photographs distribution per class

Class	Training	Validation	Test	Total
Few hours to two d	179	23	22	224
iii d	85	11	xi	107
4 to vi d	250	31	31	312
seven to 12 d	450	56	56	562
13 to 17 d	200	25	25	250
More than than 17 d	453	57	56	566
Good for you skin	95	12	12	119
Total	1712	215	213	2140

The full number of photographs estimated for the experiment is 5400; however, photographs of bruise on fingers are excluded, since the photographs contain more than one trample. In improver, despite the established protocol, some participants did not submit the photographs daily, or the photographs presented shadows or mistiness, so a total of 2140 photographs were nerveless, of which 2022 contain a bruise, and 119 photographs show healthy skin. In improver, it has been observed that bruises in the two people with darkest skin were visible until the fifth day, while in the two people with lightest pare they were visible even until mean solar day xxx.

iv.2 Implementation

The Python programming language and TensorFlow, Keras, pandas, and numpy libraries were used to build, train, validate, and exam the different versions of the learning models. The Inceptionv3, Resnet50, and MobileNet models were trained in a notebook and a virtual machine in the cloud, while the MnasNet model was trained using Google's AutoML Vision service.

As part of the training script of the bruise dating model, the preprocessed images were resized to 224 × 224 pixels.

For the transfer learning, the InceptionV3, Resnet50, and MobileNet models, included in the Keras library, pre-trained in the ImageNet dataset, were used. No transfer learning was used for the training of the MnasNet model.

In the case of cross-validation, the dataset was divided into 10 groups, which were used for training and validation. Previously 10% of the photographs by each class were separated for testing the models. In addition, the stochastic gradient descent algorithm was used for the optimization of Inceptionv3, Resnet50, and MobileNet models, with a learning rate of 0.0001, and batch size equal to of 32. The models were trained with 100, 200, and 1000 times. If the validation accurateness stopped improving for three consecutive epochs, the training was stopped, and another variant was tested.

In summary, 20 models of bruise dating were trained using four variants of neural networks, with or without cross-validation, transfer learning, and dissimilar number of grooming epochs (encounter Tabular array v). The MnasNet model compages and its parameters were determined by Google'due south AutoML Vision service, which makes an automated search for a neural network architecture optimized for mobile devices in terms of accuracy and latency [xiv]. A recurrent neural network (RNN) is used to generate the candidate architectures to be evaluated, so trains, tests the models, and feeds the RNN to generate an optimized architecture, repeating the cycle. The finally generated model consists of a DCNN trained in the specific dataset and optimized for execution in mobile devices. This model was embedded in an Android mobile awarding, for bruise dating in off-line mode. The dataset, available on request, and admission to the Google AutoML Vision service are enough to replicate the results of the all-time model.

Table 5. Trained bruise dating models

Id	Model	Cross-validation	Transfer learning	Epochs	Classes	Data augmentation
M1	InceptionV3	No	No	100	7	No
M2	InceptionV3	No	No	200	vii	No
M3	InceptionV3	No	No	1000	7	No
M4	InceptionV3	No	Yes	100	7	No
M5	InceptionV3	No	Yep	200	vii	No
M6	InceptionV3	No	Yes	1000	7	No
M7	InceptionV3	Yes	No	100	7	No
M8	InceptionV3	Yep	No	200	vii	No
M9	InceptionV3	Yes	No	1000	7	No
M10	InceptionV3	Yes	Yes	100	7	No
M11	InceptionV3	Yes	Yes	200	7	No
M12	InceptionV3	Yes	Yes	1000	seven	No
M13	Resnet50	No	No	100	7	No
M14	Resnet50	No	Yes	100	vii	No
M15	MobileNet	No	No	100	7	No
M16	MobileNet	No	Yep	100	7	No
M17	MnasNet^a	---	---	---	6^b	No
M18	MnasNet^a	---	---	---	5^c	No
M19	MnasNet^a	---	---	---	vii	No
M20	MnasNet^a	---	---	---	7	Yes

The sixteen variants of the trained models based on InceptionV3, Resnet50, and MobileNet differ in the use or not of cantankerous-validation, transfer learning, and number of training epochs. The models based on MnasNet, from M16 to M20, differ in the inclusion or not of the classes "More than than 17 days," "Healthy skin," and the use or not of data augmentation. The information augmentation for M20 was obtained by duplicating the information of the "Healthy skin" class.

4.3 Metrics

The post-obit metrics were used to evaluate and examination the learning models:

Precision (PRE). Rate of instances classified correctly.
Sensitivity (SEN). True-positive rate, that is, values classified as positive when they are positive. Correctly place instances within a class.
Specificity (SPE). Truthful-negative rate, that is, values classified as negative when they are negative. Correctly identify instances that do not vest to a class.

In this work, the goal is to obtain a model with high precision and sensitivity, since the most important affair is to classify a bruise correctly co-ordinate to its historic period. Achieving high specificity is non a priority in this case, simply it would exist convenient to achieve a balance between sensitivity and specificity.

iv.4 Results

Table half dozen shows the precision, in training and validation, obtained by the twenty models indicated in Tabular array v.

TABLE 6. Precision (PRE) for each trained model

Model	Grooming Precision (%)	Validation Precision (%)
M1	39.88	41.35
M2	86.56	40.22
M3	33.97	33.85
M4	53.57	32.14
M5	81.31	46.43
M6	89.93	42.86
M7	46.ten	32.96
M8	54.08	38.55
M9	54.91	31.28
M10	thirty.97	26.26
M11	37.77	eighteen.75
M12	56.25	56.25
M13	thirty.66	17.32
M14	96.04	43.58
M15	89.71	38.55
M16	25.55	25.70
M17	97.41	97.41
M18	96.53	96.53
M19	97.xiii	97.13
M20	97.78	97.78

Overfitting was presented for the M1-M16 models, which is explained considering the validation accuracy is much lower than the training accuracy. On the other hand, the models that nowadays greater precision in the validation are M17-M20, based on the MnasNet model (meet Table five). These models were exposed to a more detailed analysis, equally shown in Table seven, to determine the best one for bruise dating.

Table 7. Results for bruise dating models based on MnasNet

Class	M17			M18
Class	PRE %	SEN %	SPE %	PRE %	SEN %	SPE %
Few hours to 2 d	95.00%	95.00%	100.00%	95.00%	95.00%	100.00%
3 d	100.00%	100.00%	100.00%	91.00%	91.00%	100.00%
4 to 6 d	97.00%	97.00%	99.xx%	100.00%	100.00%	95.75%
7 to 12 d	100.00%	100.00%	98.00%	96.00%	96.00%	98.75%
13 to 17 d	96.00%	96.00%	99.00%	96.00%	96.00%	100.00%
More than 17 d	93.00%	93.00%	100.00%	---	---	---
Healthy skin	---	---	---	---	---	---
Average	97.00%	97.00%	99.37%	96.00%	96.00%	98.90%

Class	M19			M20
Class	PRE %	SEN %	SPE %	PRE %	SEN %	SPE %
Few hours to 2 d	100.00%	100.00%	99.33%	95.00%	95.00%	100.00%
3 d	100.00%	100.00%	100.00%	96.00%	96.00%	100.00%
4 to 6 d	100.00%	100.00%	99.33%	94.00%	94.00%	100.00%
7 to 12 d	96.00%	96.00%	100.00%	96.00%	96.00%	97.17%
xiii to 17 d	96.00%	96.00%	99.17%	96.00%	96.00%	98.50%
More than 17 d	95.00%	95.00%	98.67%	95.00%	95.00%	99.00%
Healthy skin	92.00%	92.00%	100.00%	94.00%	94.00%	99.67%
Average	97.00%	97.00%	99.50%	95.00%	95.00%	99.nineteen%

Table vii shows that M19 has the highest average precision, sensitivity, and specificity, being the model chosen for bruise dating.

Equally can be seen in the Confusion Matrix (tabular array viii), high precision was obtained for all classes, where the "Healthy skin" class is the least authentic with 92%, which is still high. For the "From seven to 12 days" class, the model has a iv% error, predicting an age of "few hours to two days," a class non next to the real course. This may be due to an error following the paradigm capture protocol. Something similar, although to a lesser extent, happens with the class "from xiii to 17 days."

Tabular array 8. Defoliation matrix for M19 (%)

Class	Predicted
Real	Few hours to 2 d	three d	4 to half dozen d	vii to 12 d	thirteen to 17 d	More than 17 d	Salubrious pare
Few hours to 2 d	100%	0%	0%	0%	0%	0%	0%
3 d	0%	100%	0%	0%	0%	0%	0%
4 to half-dozen d	0%	0%	100%	0%	0%	0%	0%
vii to 12 d	4%	0%	0%	96%	0%	0%	0%
xiii to 17 d	0%	0%	4%	0%	96%	0%	0%
More than than 17 d	0%	0%	0%	0%	v%	95%	0%
Good for you skin	0%	0%	0%	0%	0%	8%	92%

5 CONCLUSIONS

This work has introduced a model for bruise dating in living homo beings, based on deep learning, which considers a protocol for obtaining images, a preprocessing procedure, and a classification model based on deep convolutional neural networks.

The numerical results on 20 configurations of nomenclature models, tested on 213 images, show that models based on InceptionV3, Resnet50, and MobileNet accept overfitting and dating precision less than 57%. Meanwhile, MnasNet-based models achieve precision greater than 95%, the best results beingness 97% precision, 97% sensitivity, and 99.five% specificity, for the model with 7 classes and no data augmentation, a result far exceeding forty% reported in the literature to date. The results as well show that the quality of precision decreases every bit the age of the bruise increases. In the best model, this is manifested from the seventh 24-hour interval. This can be explained considering the visual information of the bruise is lost every bit time passes. These results confirm the proposed model is suitable for bruise dating, given that information technology has presented 97% precision for bruise dating on people of mestizo complexion, far above the 50% precision obtained by experts through images of white people, suggesting the model could be used for other peel colors.

A limitation of the electric current results is that they are based on images obtained in a controlled experiment and heterogeneous context, to guarantee the accuracy of the information, given the difficulty to get bruise images of cases of violence. A futurity work is extending the proposed model for some aspects of the physical violence, such equally the used object, intensity, and geographical location of the event of violence.

The main limitation of this written report and the proposed model is that it is based exclusively on images of salubrious people for bruise dating. Thus, a future work is to extend the model to include the biological variability, such as age, sexual practice, skin color, and race of the subject, but likewise location, size, depth, and caste of injury. Another futurity work is extending the model for various biological statuses (e.yard., people with chronic diseases such equally diabetes).

6 ACKNOWLEDGMENTS

The authors would like to thank Lino Gutierrez, forensic medical adviser, for sharing his cognition, experiences, and absolving our doubts about the field of study. Thanks to Jean C. Paucar equally well, for his support in the development of the prototype of the mobile awarding for bruise dating.

REFERENCES

1 World Health Organisation. Global and regional estimates of violence against women. 2013. https://www.who.int/reproductivehealth/publications/violence/9789241564625/en/. Accessed 2 Aug 2020.
2 Langlois NEI, Gresham GA. The ageing of bruises: A review and study of the colour changes with time. Forensic Sci Int. 1991; 50(2): 227– 38. https://doi.org/10.1016/0379-0738(91)90154-B.
3 Instituto de Medicina Legal del Peru. Guía médico legal de valoración integral de lesiones corporales [Medical legal guide for the comprehensive assessment of bodily injuries]. 2014;76. https://www.mpfn.gob.pe/escuela/contenido/actividades/docs/3398_1.1)_guia_lesiones_2014_final.pdf. Accessed 2 Aug 2020.
4 Ross CG, Langlois NEI, Heath Thousand, Byard RW. Farther evidence for a lack of reliability in the histologic ageing of bruises – an autopsy study. Aust J Forensic Sci. 2015; 47(2): 224– ix. https://doi.org/10.1080/00450618.2014.933486.
v Barington 1000, Jensen HE, Skovgaard K. Forensic aspects of gene expression signatures for age determination in bruises equally evaluated in an experimental porcine model. Forensic Sci Med Pathol. 2017; xiii(two): 151– 60. https://doi.org/10.1007/s12024-017-9869-2.
6 Thavarajah D, Vanezis P, Perrett D. Cess of bruise age on dark-skinned individuals using tristimulus colorimetry. Med Sci Law. 2012; 52(1): 6– 11. https://doi.org/x.1258/msl.2011.011038.
seven Pilling ML, Vanezis P, Perrett D, Johnston A. Visual assessment of the timing of bruising by forensic experts. J Forensic Leg Med. 2010; 17(3): 143– nine. https://doi.org/10.1016/j.jflm.2009.ten.002.
8 Stam B, van Gemert MJC, van Leeuwen TG, Teeuw AH, van der Wal AC, Aalders MCG. Can color inhomogeneity of bruises be used to institute their historic period? J Biophotonics. 2011; 4(ten): 759– 67. https://doi.org/10.1002/jbio.201100021.
9 Grossman SE, Johnston A, Vanezis P, Perrett D. Can we appraise the historic period of bruises? An attempt to develop an objective technique. Med Sci Police. 2011; 51(3): 170– 6. https://doi.org/x.1258/msl.2011.010135.
10 Bechar MEA, Settouti Northward, Barra V, Chikh MA. Semi-supervised superpixel classification for medical images segmentation: Application to detection of glaucoma disease. Multidimens Syst Signal Procedure. 2018; 29(3): 979– 98. https://doi.org/x.1007/s11045-017-0483-y.
xi Pal A, Garain U, Chandra A, Chatterjee R, Senapati Southward. Psoriasis pare biopsy paradigm segmentation using Deep Convolutional Neural Network. Comput Methods Programs Biomed. 2018; 159: 59– 69. https://doi.org/x.1016/j.cmpb.2018.01.027.
12 Zhang Due west, Ren Fifty, Wang L. A method of deep belief network paradigm classification based on probability mensurate rough ready theory. Int J Blueprint Recognit Artif Intell. 2018; 32(11): 1850040. https://doi.org/10.1142/S0218001418500404.
13 Hekler A, Utikal JS, Enk AH, Berking C, Klode J, Schadendorf D, et al. Pathologist-level classification of histopathological melanoma images with deep neural networks. Eur J Cancer. 2019; 115: 79– 83. https://doi.org/10.1016/j.ejca.2019.04.021.
xiv Tan M, Chen B, Pang R, Vasudevan V, Sandler Thou, Howard A, et al. MnasNet: Platform-aware neural compages search for mobile. In: Proceedings of the 2022 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019): 2022 June xv–21; Long Beach, CA. Piscataway, NJ: IEEE, 2019; 2815– 23. https://doi.org/ten.1109/CVPR.2019.00293.
15 Scafide KN, Sheridan DJ, Taylor LA, Hayat MJ. Reliability of tristimulus colourimetry in the assessment of cutaneous trample colour. Injury. 2016; 47(6): 1258– 63. https://doi.org/ten.1016/j.injury.2016.01.032.
sixteen Mesli V, Le Garff East, Marchand Eastward, Labreuche J, Ramdane North, Maynou C, et al. Determination of the age of bruises using a bilirubinometer. Forensic Sci Int. 2019; 302: 109831. https://doi.org/10.1016/j.forsciint.2019.05.047.
17 Scafide KN, Sheridan DJ, Downing NR, Hayat MJ. Detection of inflicted bruises by alternate light: Results of a randomized controlled trial. J Forensic Sci. 2020; 65(4): 1191– 8. https://doi.org/x.1111/1556-4029.14294.
18 Wang Southward, Tao D, Yang J. Relative attribute SVM+ learning for age estimation. IEEE Trans Cybern. 2016; 46(3): 827– 39. https://doi.org/10.1109/TCYB.2015.2416321.
19 Avuçlu E, Başçiftçi F. New approaches to make up one's mind historic period and gender in image processing techniques using multilayer perceptron neural network. Appl Soft Comput J. 2018; 70: 157– 68. https://doi.org/x.1016/j.asoc.2018.05.033.
twenty Chen L, Fan C, Yang H, Hu S, Zou 50, Deng D. Face age nomenclature based on a deep hybrid model. Indicate, Image Video Procedure. 2018; 12(8): 1531– 9. https://doi.org/x.1007/s11760-018-1309-vi.
21 Saidi M, Bechar MEA, Settouti Northward, Chikh MA. Instances selection algorithm by ensemble margin. J Exp Theor Artif Intell. 2018; xxx(three): 457– 78. https://doi.org/10.1080/0952813X.2017.1409283.
22 Rattani A, Reddy N, Derakhshani R. Convolutional neural networks for gender prediction from smartphone-based ocular images. IET Biometrics. 2018; 7(5): 423– thirty. https://doi.org/10.1049/iet-bmt.2017.0171.
23 Cho D, Tai Y-W, Kweon IS. Deep convolutional neural network for natural Prototype matting using Initial alpha mattes. IEEE Trans Image Process. 2018; 28(3): 1054– 67. https://doi.org/10.1109/TIP.2018.2872925.
24 Hermessi H, Mourali O, Zagrouba E. Deep feature learning for soft tissue sarcoma nomenclature in MR images via transfer learning. Expert Syst Appl. 2019; 120: 116– 27. https://doi.org/ten.1016/j.eswa.2018.eleven.025.
25 Perez 50, Wang J. The effectiveness of data augmentation in paradigm nomenclature using deep learning. ArXiv171204621 Cs. Dec. 2017. https://arxiv.org/pdf/1712.04621.pdf. Accessed 31 Aug 2020.
26 Szegedy C, Vanhoucke V, Ioffe Southward, Shlens J, Wojna Z. Rethinking the inception compages for computer vision. In: Proceedings of the 2022 IEEE Briefing on Computer Vision Pattern Recognition; 2022 June 27–30; Las Vegas, NV. Piscataway, NJ: IEEE, 2016; 2818– 26. https://doi.org/10.1109/CVPR.2016.308.
27 He M, Zhang X, Ren S, Sun J. Deep balance learning for image recognition. In: Proceedings of the 2022 IEEE Briefing on Figurer Vision Pattern Recognition; 2022 June 27–30; Las Vegas, NV. Piscataway, NJ: IEEE, 2016; 770– 8. https://doi.org/ten.1109/CVPR.2016.90.
28 Howard AG, Zhu M, Chen B, Kalenichenko D, Wang West, Weyand T, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications. ArXiv1704.04861 Cs. April. 2017. https://arxiv.org/abs/1704.04861. Accessed 31 Aug 2020.
29 INEI. Indicadores de violencia familiar y sexual [Indicators of family and sexual violence]. 2017; 2000– 17. https://world wide web.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1465/libro.pdf. Accessed 2 Aug 2020.
30 Vidovic L, Milanic Chiliad, Majaron B. Objective characterization of bruise evolution using photothermal depth profiling and Monte Carlo modeling. J Biomed Opt. 2015; xx(ane):017001. https://doi.org/10.1117/1.JBO.20.1.017001.
31 Harris C, Alcock A, Trefan L, Nuttall D, Evans ST, Maguire S, et al. Optimising the measurement of bruises in children across conventional and cross-polarized images using segmentation analysis techniques in Paradigm J, Photoshop and circle diameter measurements. J Forensic Leg Med. 2018; 54: 114– 20. https://doi.org/10.1016/j.jflm.2017.12.020.
32 Maguire Due south, Mann MK, Sibert J, Kemp A. Can you age bruises accurately in children? A systematic review. Arch Dis Child. 2005; 90(2): 187– 9. https://doi.org/ten.1136/adc.2003.044073.
33 Llajaruna Chiliad, Mauricio D, Gutierrez Fifty. Datación de equimosis en el peritaje médico legal peruano mediante redes neuronales artificiales y procesamiento de imágenes [Dating of ecchymosis in the Peruvian legal medical expertise through artificial neural networks and processing of images]. 2012; 366– 77. http://www.din.uem.br/sbpo/sbpo2012/pdf/arq0214.pdf. Accessed two Aug 2020.

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