DEEP LEARNING ALGORITHMS FOR CONVOLUTIONAL NEURAL NETWORKS (CNNS) USING AN APPROPRIATE CELL

International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN (P): 2249–6831; ISSN (E): 2249–7943 Vol. 12, Issue 1, Jun 2022, 47–60 © TJPRC Pvt. Ltd.

DEEP LEARNING ALGORITHMS FOR CONVOLUTIONAL NEURAL NETWORKS (CNNS) USING AN APPROPRIATE CELL-SEGMENTATION METHOD JALAWI ALSHUDUKHI University of Ha'il, College of Computer Science and Engineering, Department of Computer Science, Kingdom of Saudi Arabia ABSTRACT Cancer is one of the most common and deadly diseases in the world, accounting for a significant number of fatalities each year. For this condition, early detection and differentiation are crucial. Specialists are increasingly able to execute diagnoses more quickly because of image analysis and computer-assisted diagnosis. Despite this, many computational approaches still face difficulties in the identification, segmentation and categorization of cells in histopathology images.

For this work, the goal was to aid specialists in the diagnosis and classification of cancer by providing them with the technological support they needed to do so. Convolutional neural networks (CNNs) Using an appropriate cellsegmentation method, cells in histopathology images were separated. Deep learning algorithms outperformed traditional cell segmentation techniques when combined with adequate image processing and convolutional neural those mentioned in bibliographies and publications that utilized similar ways to use deep learning to implement similar approaches to ours.

KEYWORDS: Cancer Images, CNN, HAR Images, Nucleus Segmentation & Architecture

Original Article

network architecture, according to the findings of this research. This technique was found to be more successful than

Received: Jan 07, 2022; Accepted: Jan 27, 2022; Published: Feb 07, 2022; Paper Id: IJCSEITRJUN20226

INTRODUCTION In the analysis of images for the diagnosis and classification of cancer, specialists seek to define the regularity of cell borders, their shapes and distributions. To determine these characteristics, first, a cell segmentation process must be carried out, that is, they must be discriminated from the rest of the image. Therefore, it is crucial to accurately identify such regions in HAR (High Resolution Histopathology) images [1]. The precision in the quantification of cancerous tissue in HAR images is often affected by the conditions and the type of sample. Some examples are: cells overlapping or in contact, noise disturbances in the contours of the cells, and blurring due to zoom in the digitization process. Various CAD (Computer Aided Diagnosis) techniques have been applied in the past to solve the problem of correct identification and segmentation of cells in HAR images. The procedure of most of the traditional nucleus segmentation methods can be divided into two steps: first, the nuclei are detected and then their contours are obtained. After this procedure, it is possible to derive different morphometric quantifiers, such as, for example, their area. One of the most widely used and simplest methods to detect nuclei in HAR images consists of using the Otsu method to estimate the intensity threshold that allows the nuclei to be separated from the rest of the image. However, it has a limitation, it only works under the scenario that the nuclei in the image have significant differences in intensity with respect to the background. Furthermore, it assumes uniformity in the intensity of the pixels of the objects to be detected. Finally, this method is a technique not very robust to noise

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since it is based purely on the value of the intensity of each of the pixels independently of the environment. Other approaches proposed in the literature use different clustering techniques such as K-means clustering [3] and Graph Cuts.

More elaborate approaches introduce different characteristics of the nuclei to aid the segmentation process, such as processing with colour mixing and morphological operators [2]. However, these methods are prone to failure in the face of a wide morphological range of nuclei and strongly depend on the initialization of their parameters, which are set manually.

Given that the shapes of the nuclei are very diverse, the approaches based on deep learning (DL) models are becoming increasingly attractive. These models, unlike the previous ones, learn to identify the most relevant characteristics of the problem automatically to solve classification and / or segmentation tasks. As an example, he has succeeded in segmenting the pixels corresponding to the cell nuclei on an image, classifying each of them into two categories: nucleus or background [4]. As a result, the procedure of manually determining the system's most important characteristic parameters is avoided, resulting in an approach that is more resilient than the techniques indicated above. In numerous areas of image processing, such as detection, segmentation, and classification, deep learning algorithms have recently been proven to achieve accuracy equivalent to that of humans. As a result, they're employed to cooperate on research and/or tasks in the field of biomedical imaging, such as cardiac muscle segmentation, cell segmentation and epithelia, tumor identification, lymphocytes and mitosis, lymphoma classification, and cancer nuclei classification, to name a few [6-9]. Due to the importance of correct identification and segmentation of cells in the diagnosis of cancer, this project will investigate and develop technological supports that meet these needs [5]. Given the importance of the detection and classification of nuclei in HAR images to quantify and diagnose cancer, the main objective of this integrative project is to provide technological support to patients. Pathologists which allows them to identify and segment nuclei in HAR images. For this purpose, different language and library options such as C ++, Python, Tensor flow and Keras among others, will be studied.

2.0 METHODOLOGY 2.1. Materials The database with which we worked is detailed, it consists of 121 images of positive estrogen receptors for breast cancer (ER + BCa) with a resolution of 2000 x 2000 scanned by a microscope with a magnification of 40x, that is, 0, 16µm / pixel. Among the total of these images are about 12,000 manually segmented nuclei. Other databases with cellular images of similar morphologies were added to the ER + BCa database in order to increase the robustness of the network. They were acquired through Kaggle [10]. The databases added by Kaggle were: images of human cells of colon cancer HT29 (HT29), images of tissues stained with hematoxylin and eosin (H&E), and images obtained from two microscopes for two types of cells stained with Hoechst 33342 (ISBI). In Figure 1 you can see representative images of each database obtained, where the upper left image belongs to ER + BCa, the upper right image belongs to HT29, the lower left image belongs to H&E and the lower right image belongs to ISBI.

Figure 1: Arbitrary Images of the Databases Selected Through Kaggle.

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2.2. Network Architecture Inspired by the CNN architecture proposed by [11-14] where they solve the problem of myocardial muscle segmentation, this technique was extrapolated to that of segmentation of cell nuclei in HAR images to carry out the objective of this work. A representative scheme of this network can be seen in Figure 2, it is based on the architecture of the U-Net [15] with two inputs and two outputs. Within the proposed network (Figure 2) you can see two inputs, one upper and one lower, and two outputs (upper and lower). This architecture has four levels, unlike the original U-Net which has five, each level refers to the height and width sizes of the activation maps (642 for the first level, 322 for the second, etc.). As in the first half of the network, these dimensions are reduced at each level, this stage is called encoding. The other half of the architecture performs the reverse process, which is why it is called the decoding stage.

Figure 2: Proposed Architecture to Solve the Nucleus Segmentation Problem in HAR Images.

In the proposed network, both inputs are used during the training stage, where the image (conventional input) and its segmentation respectively enter. Regarding the outputs, in the upper one (conventional output) the segmented input image is obtained, and in the lower one, it is used only during the training stage. The purpose of the lower encoding, used in the learning stage, is to help the network follow a reference of the encoded core structure.

Each level, both in the encoding and decoding stage, is made up of two CLs with 3 x 3 filters and a Max Pooling or Un-pooling layer depending on whether it is in the encoding or decoding stage on respectively. www.tjprc.org

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Finally, it can be seen that there is a concatenation for each level between the encoding and decoding stage of the conventional part of the network, managing to avoid the vanishing problem [16-19] that the training has of multilayer networks improving their reconstruction. Furthermore, it can also be observed that both inputs carry out an encoding stage, but only one of them carries out the decoding stage. The function that the bottom entry has is to minimize a subtraction Quadratic between both architectures at the last level of coding so that the upper part can follow the encoded structure of the nuclei in the lower part as mentioned above.

2.3. T64 Pre-Processing of the Database There are three important factors within the training of a CNN that limit the memory capacity of a computer, which are: the dimensionality of the input, the amount of network parameters and the mini-batch chosen from the training. Therefore, these factors must be chosen with caution to obtain the best performance of the network. In particular, in this section we will focus on the treatment of the image that was used in order not to have large dimensions at the entrance, further limiting the other factors that could worsen said performance. There are several ways to reduce the size of the input image to the network, for example, it can be resized, cropped or a combination of both. There is no single method or criteria to choose the most convenient option. Given that in some images not all the segmented nuclei are found, as shown in Figure 3, we proceeded to carry out the treatment of images proposed by [20-24]. It consists of cropping the images into 64 x 64 sizes in a particular way. The idea is to take an image for each nucleus that is segmented, with which as many images as segmented nuclei will be obtained. The size of 64 x 64 is due to the fact that it is sufficient for a complete nucleus to enter without protruding from the image, which will capture the greatest number of characteristics of the nucleus, such as, for example, the different intensities that compose it, its shape, the qualities of the edges, etc.

Figure 3: HAR Image of the ER + BCa Database with its Respective Partial Segmentation.

The detection of the position of the segmented nuclei for clipping will be carried out through the segmented images, since in them, each segmented nucleus is found positioned in the same coordinates as the nucleus in the original image. An arbitrary image obtained after making these cuts is shown in Figure 4.

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Figure 4: On the Left, an Arbitrary image of the databases Generated due to the 64 x 64 Clipping can be Observed, and on the Right is their Respective Segmentation. It can also be Observed that 2 Superimposed Nuclei are not Segmented. (b) Image and its Respective Result after Applying the Filter (c) On the Left you can see an Arbitrary Image of the Databases Generated due to the 64x64 cutout without any Kernel, and on the Right their Respective Segmentation.

To obtain a variety of images in the training data both with and without nuclei, 64 x 64 sections of the original image without nuclei were taken. The implemented way to carry out the obtaining of these images is by applying an intensity filter in the image, since the nuclei have, in part, a color of less intensity (more black) than the tissue. So, if you want to obtain images of tissue without a nucleus, sections of the image are taken where this filter did not mark any intensity greater than a certain value. In Figure 4 (b) you can see an image and its result after applying the filter. The intensity value taken for the filter was the average of the pixels that they belong to the segmented nuclei. A 64 x 64 clipping is shown on the left in Figure 4 (c) where the filter did not detect cell nuclei intensities. On the right, the same section is observed, but of the segmented image, and it can be corroborated in the same that there is no highlighted nucleus.

After applying the filters and making all the corresponding cuts, a total of 35,704 images were obtained, of which only 1812 images were found without nuclei. Finally, each cropped image was normalized with the standard score normalization, where for each pixel pi,j in the coordinate (𝑖, 𝑗) and depth 𝑘 (due to the color of each layer) we have: 𝑝𝑛𝑜𝑟𝑚,𝑖,𝑗𝑘 =

𝑝𝑖,𝑗𝑘 −𝑝 ̅​̅​̅​̅ 𝑘 𝜎𝑘

Where pk and σk is the mean and the corresponding standard deviation of the intensity of the pixels in the color layer k.

2.4. Frameworks There are various DL computational environments for the implementation and training of deep neural networks (DNNs).

These open source environments contain software libraries to facilitate the implementation, use and training of DNNs. Among the most popular [25-28] are: Tensorflow, Theano, PyTorch, Caffe2 and Keras, which were developed from different sources. The environment used to carry out the objective of this project was Keras, since it is specially designed to allow experimentation with DNNs in a short time. In addition, it has as (backend) the Tensorflow and Theano libraries, giving the possibility of using either of the two.

2.5. Training and Validation Characteristics Once the T64 treatment was carried out, a total of 35,704 64 x 64 color images were obtained. Among them are 3624 from the database, 12 388 from ER + BCa, 5 585 from HT29 and 14 107 from H&E. On the other hand, a total of 1 812 images without nuclei were obtained. Each database (ER + BCa, HT29, H&E, ISBI) was partitioned into two subgroups, one of 70% that was used only for training and 30% for validation. To avoid some type of bias in the data collection, the images for validation and training were taken randomly in all the databases used. Thus, 24,993 images were used in the training www.tjprc.org

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stage, and 10,711 in the validation stage, with a proportion of pixels occupied by nuclei of 27.66% and 27.50% respectively.

3.0 RESULTS AND DISCUSSIONS 3.1. U-Net and Pre-Processing In this section we will show the results of the training of the U-Net network [11] with two types of image treatments, the one proposed by the project (T64) in 2.3 and the one described below on: 3.2 Image pre-Processing T256 This treatment was performed as inspiration when observing those carried out in [29]. They have chosen to make cuts of 128 x 128, 200 x 200 and 512 x 512 respectively without resizing so as not to lose information. It was decided to carry out an average between the values found and the value closest to the set of values formed by powers of 2 (2, 4,8,...) was chosen, reaching a value of 256 x 256. This is done since generally in network architectures Marooning / Unspooling layers are included, which enlarge or reduce the input dimensions by a factor, which in most cases is 2. The procedure to perform the cuts are as follows: 256 x 256 sections are taken with a step of 128 across the width, when the entire row is finished, it advances 128 to the top and the previous process is repeated. This is carried out successively until completing the entire image path, thus obtaining all the corresponding cuts. If we execute this procedure, for example, in an image of dimensions 2000 x 2000 we end up obtaining 256 images. Figure5 shows a HAR image from the ER + BCa database with its respective first 256 x 256 clipping.

Figure 5: On the Left, you can see a Complete Image of the ER + BCa database with Dimensions of 2000 x 2000 x 3, and on the Right a Clipping of it of 256 x 256 x 3.

3.3 Results with U-Net The U-Net is a standard architecture used to solve segmentation problems in biomedical images [11] obtaining satisfactory results by the specialists who use it. In addition, it is an FCN network that won the great challenge for the automated detection of caries by computer in a bitewing radiograph in the ISBI challenge of 20151. It is for these reasons that has decided to work with it. As we mentioned in the previous section (2.2), this network is divided into two stages, one for encoding and one for decoding. Each of them has five levels, which refer to the size of the activation maps (height and width). Each level is made up of two CLs with 3 x 3 filters, a unit step and padding, followed by a Max-Pooling or Unpooling according to the encoding or decoding stage respectively. A representative diagram of the U-Net is shown in Figure 6.

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Figure 6: U-Net network, with an Encoding and a Decoding Stage, each with 5 Levels and each Level with 2 CL plus a Max-Pooling / Unpooling (up-conv). This Image was Obtained from [11] of Ordinates, the Precision is shown through the Dice Index on the Validation Data, and on the Abscissa Axis each Time of Training.

In order to determine which of the data processing resulted in better network performance, two training sessions were carried out with the U-Net, one with the T64 treatment and the other with the T256 on the images from the ER + BCa database. The characteristics and training parameters are shown in table 1.

Table 1: U-Net Architecture and Training Parameters Treatment T256 T64 Network input size 256 x 256 x 3 64 x 64 x 3 Training algorithm Ahmad (η = 1e-4) Cost Function Jaccard-Distance Number of network parameters 31 031 745 Number of epochs 100 Mini-batch size 32 72 Databases used ER+BCa Number of images 13 230 12 388 Amount of data for training 70% Amount of data for validation 30%

In the results, it can be observed that the T64 treatment showed a better performance compared to the T256, this may be due to the fact that the T64 method ensures that in each image all the characteristics of the nucleus since at least one is always found completely. Due to these results, we continued to work with the T64 treatment in the rest of the work.

3.4. Increasing Precision with Increasing Data There are a variety of methods for increasing the quantity of data in order to improve the network's resilience, such as performing translations, inversions on one axis, introducing noise, and so on. We sought to see whether adding comparable databases of cell nuclei with similar morphologies (such as nucleus size and forms, etc.) to the U-Net for the ER + BCa database would increase its performance. As a result, the bases stated in 1 were added: HT29, ISBI, and H&E.

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After performing the T64 treatment to each of the new databases, we conducted three U-Net trainings, each with the identical set of parameters from table 1 of the T64 column. The initial training used the datasets ER + BCa and ISBI, followed by ER + BCa, ISBI, and HT29, and finally, ER + BCa, ISBI, HT29, and H&E. Table 2 presents the training settings; note that the validation data were only collected using the ER + BCa database. The abscissa axis displays the Dice index for each of the above-mentioned training sessions, whereas the ordinate axis shows the outcomes for each of the above-mentioned training sessions.

Table 2: U-Net Architecture and Training Parameters for each Data Set Training Parameters ER+BCa ISBI ER+BCa ISBI Databases used ER+BCa ISBI HT29 HT29 H&E Network input size; Training algorithm 64 x 64 x 3 Cost Function Adam (η = 1e-4) Number of network parameters JD Number of epochs 31 031 745 Mini-batch size 16012 30119 35704 Number of images 16012 30119 35704 70 % ER+BCa 70 % ER+BCa 70 % ER+BCa 100 % ISBI 100 % ISBI Amount of data for training 100 % ISBI 100 % HT29 100 % HT29 100 % H&E Amount of data for validation 30 % ER+Bca

As the present project did not have the time to carry out this comparative method, since each training takes more than 24 hours, there are other simpler ones that may be useful. For example, a discrimination can be made according to: the last value or maximum value obtained during training, or an average can be calculated from the data set formed by the values associated with the steady state. If we take the average method, the results for each training are as follows: –Database ER + BCa: 0.845 –ER + BCa and ISBI databases: 0.847 –ER + BCa, ISBI and HT29 databases: 0.850 –ER + BCa, ISBI, HT9 and H&E databases: 0.851 For any of the validation methods, the associated curve for the set of the four databases has the best precision.

Although the improvement is half a percentage point, no reason was found to rule out their use. Therefore, from this training, all subsequent ones were carried out using the four mentioned data sets (ER + BCa, ISBI, HT9 and H&E). We tried adding more sets of images to see if the result continued to improve, so another base was added to the previous ones.

The images added are part of the “BBBC038” database offered by the Broad Institute for the Data Science Bowl 20182, some copies of it are shown in Figure 7. After applying the T64 treatment to the BBBC038 database, a total of 26 619 images were obtained in total. The result was obtained using the five databases (ER + BCa, ISBI, HT9, H&E and BBBC038), to which the result was obtained and it was added for the case of ER + BCa, ISBI, HT9 and H&E. The mean of the data set formed by the interval from epoch 40 to the last (100) is 0.849. As it is not greater than that obtained by ER + BCa, ISBI, HT9 and H&E, the BBBC038 base is discarded for future training. Finally, we wanted to observe how the result changes if in the validation 30% of each database is taken, we also wanted to use another cost function to observe if

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the training depends significantly on the same. Therefore, a last test was carried out with the U-Net, with cost functions "Jaccard Distance” and “Mean Squared Error” with λ = 1.

The values obtained for the last epoch were 0.877 and 0.863 for JD (Jaccard-Distance) and MSE (Mean Squared Error) respectively. Concluding that the cost function JD is more adequate for the resolution of our problem for the case of the U-Net. 3.3. Results with CapsNet CapsNet are relatively new compared to the other architectures. It was decided to work with them since they have shown to have an ability to maintain the spatial relationships of the characteristics learned from the image that the other architectures do not have. The network used was a modification of the network proposed by Hinton to solve the problem of classification of handwritten digits. The Hinton network is designed to work with 28 x 28 images in classification and reconstruction tasks. Therefore, to extrapolate this network to our problem, the classification stage was removed, since we do not want to classify, and the last FC layer was changed with 784 (28 * 28) neurons, by a 4096 (64 * 64) to rebuild our desired output. Two trainings were carried out with this architecture, they have the network and training parameters shown in table 3: Table 3: Modified CapsNet Architecture and Training Parameters.

Network input size 64 x 64 x 3 Training algorithm Adam (η = 1e-4) Cost Function MSE Number of network parameters 33 770 240 Number of epochs 100 Mini-batch size 72 Databases used ER + BCa ISBI HT29 H&E Number of images 35 704 Amount of data for training 70% Amount of data for validation 30%

The results of the training for this architecture and the U-Net results obtained previously that were executed with the same parameters were added. The value obtained for the last era of CapsNet was 0.744, which is much lower than that of U-Net. On the other hand, the same CapsNet training was also tested but using the JD cost function, giving a result for the last epoch of 0.492. With these results, it can be concluded that CapsNet performs better with the cost function MSE than with JD, unlike U-Net, which is the opposite. However, U-Net performed better than CapsNet with both cost functions.

3.4. U-Net Results with CapsNet in Parallel Although the recently proposed CapsNet did not perform better than U-Net, it was hypothesized that if the results can be improved by forming a network containing both architectures. Therefore, a network was designed that combines U-Net and CapsNet in parallel. To carry out this concatenation, it was necessary to make some modifications in both architectures: since the number of parameters of both architectures has a value close to 60 million, which exceeds the computational memory in the PCs used, He eliminated the last level from the U-Net, leaving 4 levels. On the other hand, for CapsNet, an FC was removed and a CL was added so that the concatenation with the U-Net is consistent. A representative scheme of this network is shown in Figure 7. www.tjprc.org

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The Cost function chosen was MSE + JD, since the U-Net had a better performance with JD and in CapsNet with MSE. Furthermore, it is proposed in [13] to use MSE for CapsNet. The results were obtained for 100 epochs, where the UNet result was added. The value obtained for the last epoch is 0.876, being the same very close to the value of the U-Net (0.877), but it can be seen in the graph that the U-Net is always above of the other network in the last 20 times that the steady state is reached. Therefore, until now, the U-Net continues to perform better than the rest of the networks. Small modifications to this latest combined architecture have also been tried, such as, for example, changing the cost function by removing the MSE, or concatenating both networks in the u´ Last CL. Once both networks were trained, where the result of the U-Net was added to it. The best performing network among the three shown was the CapsNet combined with the UNet with the JD cost function, where a Dice value of 0.878 and a peak value m were obtained for the last period. Maximum of 0.881 for epoch 89. This is the first designed network that has a better performance than the U-Net. In the last results obtained, there is no significant difference between the architectures mentioned.

Figure 7: Representative Diagram of the Parallelization of the Modified CapsNet and U-Net Architecture.

4.0 RESULTS OF THE PROPOSED NETWORK The proposed network that was described in section 2.2 was tested, it was trained with the parameters described in table 4 with the difference that, for the conventional output, it was used or the cost function of JD and for the output of the fourth level the cost function MSE was used with λ = 1e4 and λ = 1. The results obtained from it, were added the U-Net result with the Capnset and JD cost function obtained earlier. It can be seen that the proposed U-Net with λ = 1e4 was the one that performed the best with respect to the rest, reaching a value of 0.882 for the last time and leaving the U-Net in second place with. As explained in [9], the idea of tracking the U-Net at the last level over the lower-stage encoded core structure is what has made this network perform the best or of all those implemented. Finally, to be able to compare the results with [9,13], the proposed network was tested again using the same parameters as table 4, but the validation data was 20% of the base of ER + BCa data. The training strategy was that of 5-foldings using F1-score as a validation metric. The precision of each k-folding was 0.847; 0.852; 0.853; 0.855 and 0.857 obtaining an average of 0.852 and that of the mentioned

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appointment was 0.842.

Table 4: Modified CapsNet Architecture and Training Parameters & Parameters of the Architecture and Training of the Proposed Network Network input size 64 x 64 x 3 Training algorithm Adam (η = 1e-4) Cost Function JD and MSE Number of network parameters 12 381 569 Number of epochs 100 Mini-batch size 72 Databases used ER + BCa ISBI HT29 H&E Number of images 35 704 Amount of data for training 70% Amount of data for validation 30%

CONCLUSIONS From the results obtained by the image treatments, it can be concluded that, despite the fact that the two treatments had the same normalization method, the fact of cropping the images capturing all the characteristics of al less one nucleus resulted in better performance than cropping larger sections where what is obtained in each image is arbitrary, for both nuclei and tissues. In addition, since the images are much smaller in size, the mini-batch can be enlarged, with which the training of the network turns out to be much faster since, at each time, the Backpropagation algorithm is performed fewer times and the parallelism of the evaluation of each mini-batch image is increased. On the other hand, it is left for future work to corroborate that the precision does not change for a validation set where each image has a random content of the tissue with the nuclei and not of a selected content as was done in treatment T64.

If the classic U-Net and CapsNet networks are compared, we can observe that for the task of segmentation of cell nuclei, the U-Net managed to have a better performance than the CapsNet, since it was designed for another contextualization. Despite this, combining both architectures obtained a better result than U-Net. This may be due to the fact that each architecture stores the characteristics of the nuclei in a different way, being able to learn different qualities of the cellular nuclei in each network.

The architecture proposed in the present work, not only had the best performance of all those implemented during the investigation, but the number of parameters of the same is an order of magnitude less than the rest of the networks used. But, on the other hand, we have one more parameter that we must minimize (λ), where it was observed that different results are obtained for different values4.5. This implies that a way must be sought to minimize the value of this parameter to obtain the best possible performance of the network. If not, it must be found iteratively by training the network several times to find it, which can take a long time.

Finally, it is concluded that the objective of the work described in the section on objectives1.2 was achieved. The described procedure achieved a Dice precision close to 88.2% using 30% of data as validation and 85.2% of F1 - score using the 5-foldings strategy with 20% of ER + BCa for validation. If we compare it with [13], which used the same database, treatment of images similar to the T64 and as an architecture model of FCN (Fully Convolutional Networks) the U-Net, our method exceeds by 1.2% the result obtained by them.

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FUNDING This study did not receive any funding from any source.

CONFLICTS OF INTEREST The author declares no conflict of interest.

DATA AVAILABILITY The data underlying the results presented in the study are available within the manuscript.

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Impact Factor (JCC): 11.1093

NAAS Rating: 3.76