Efficient Lung Ultrasound Classification.

A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one.

Segmentation of COVID-19 Chest CT Images Based on SwishUnet

Coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has been spreading since 2019 until now. Chest CT-scan images have contributed significantly to the prognosis, diagnosis, and detection of complications in COVID-19. Automatic segmentation of COVID-19 infections involving ground-glass opacities and consolidation can assist radiologists in COVID-19 screening, which helps reduce time spent analyzing the infection. In this study, we proposed a novel deep learning network to segment lung damage caused by COVID-19 by utilizing EfficientNet and Resnet as the encoder and a modified U-Net with Swish activation, namely swishUnet, as the decoder. In particular, swishUnet allows the model to deal with smoothness, non-monotonicity, and one-sided boundedness at zero. Three experiments were conducted to evaluate the performance of the proposed architecture on the 100 CT scans and 9 volume CT scans from Italian the society of medical and interventional radiology. The results of the first experiment showed that the best sensitivity was 82.7% using the Resnet+swishUnet method with the Tversky loss function. In the second experiment, the architecture with basic Unet only got a sensitivity of 67.2. But with our proposed method, we can improve to 88.1% by using EfficientNet+SwishUnet. For the third experiment, the best performance sensitivity is Resnet+swishUnet with 79.8%. All models with SwishUnet have the same specificity where the value is 99.8%. From the experiments we conclude that our proposed method with SwishUnet encoder has better performance than the previous method © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.

OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification.

The deep learning approach has recently attracted much attention for its outstanding performance to assist in clinical diagnostic tasks, notably in computer-aided solutions. Computer-aided solutions are being developed using chest radiography to identify lung diseases. A chest X-ray image is one of the most often utilized diagnostic imaging modalities in computer-aided solutions since it produces non-invasive standard-of-care data. However, the accurate identification of a specific illness in chest X-ray images still poses a challenge due to their high inter-class similarities and low intra-class variant abnormalities, especially given the complex nature of radiographs and the complex anatomy of the chest. In this paper, we proposed a deep-learning-based solution to classify four lung diseases (pneumonia, pneumothorax, tuberculosis, and lung cancer) and healthy lungs using chest X-ray images. In order to achieve a high performance, the EfficientNet B7 model with the pre-trained weights of ImageNet trained by Noisy Student was used as a backbone model, followed by our proposed fine-tuned layers and hyperparameters. Our study achieved an average test accuracy of 97.42%, sensitivity of 95.93%, and specificity of 99.05%. Additionally, our findings were utilized as diagnostic supporting software in OView-AI system (computer-aided application). We conducted 910 clinical trials and achieved an AUC confidence interval (95% CI) of the diagnostic results in the OView-AI system of 97.01%, sensitivity of 95.68%, and specificity of 99.34%.

COVIDEffiNet: Pulmonary Diseases and COVID-19 Detection from Chest Radiographs Using EfficientNet Deep Learning Model

There have been attempts made previously to classify and determine the diagnosis of a disease of a patient based on the X-rays and computed tomography images of various parts of the body. In the field of lung disease diagnosis, there have been attempts to identify lungs infected with pneumonia, COVID-19, and tuberculosis, either individually classifying them into two groups of positive and negative of the given disease or in groups with multiple classes. These methods and approaches have used various deep learning models like CNNs, ResNet50, VGG19, Inception V3, MobileNet_V2, hybrid models, and ensemble learning methods. In this paper, we have proposed a model that takes an X-ray image of the lungs of the patients as input and classifies the result as one of the following classes: tuberculosis, pneumonia, COVID-19, or normal, that is, healthy lungs. What we have used here is transfer learning, with our base model being EfficientNet which gives an accuracy of 93%. For this, we have used different datasets of X-ray images of patients with different lung ailments, namely pneumonia, tuberculosis, and COVID. The dataset consists of images in four categories, the above-mentioned three diseases and a fourth category of normal healthy lungs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Cognitive Computing in diagnosis of Covid-19 from CT scan and radiograph images: A comparative study

The dreadful coronavirus has not only shattered the lives of millions of people, but it has also placed enormous strain on the whole healthcare system. In order to isolate positive cases and stop the disease from spreading, early detection of COVID-19 is crucial. Currently, a laboratory test (RT-PCR) on samples collected from the throat and nose is required for the official diagnosis of COVID-19. Specialized tools are needed for the RT-PCR test, which takes at least 24 hours to complete. It may often provide more false negative and false positive results than expected. Therefore, using X-ray and CT scan images of the individual's lung, COVID-19 screening can be used to support the conventional RTPCR methods for an accurate clinical diagnosis. The importance of chest imaging in the emergence of this lung illness has been recognized. Images from the computed tomography (CT) scan and chest X-ray (CXR) can be used to quickly and accurately diagnose COVID-19. However, CT scan pictures have their own drawbacks. In order to assess the effectiveness of chest imaging approaches and demonstrates that CXR as an input may compete with CT scan pictures in the diagnosis of COVID-19 infection using various CNN based models, this article thoroughly covers modern deep learning techniques (CNN). For CXR and CT scan pictures, we have evaluated with ResNet, MobileNet, VGG 16, and EfficientNet. Both chest X-ray (3604 Images) and CT scans (3227 images) from publicly accessible databases have been evaluated, and the experimental outcomes are also contrasted. © 2022 IEEE.

Customized EfficientNet for Histopathology Image Representation

In recent years, and amplified by the COVID-19 pandemic, the digitization of pathology has gained a considerable attention. Digital pathology provides crucial advantages compared to conventional light microscopy, including more efficient workflows, more accurate diagnosis and treatment planning, and easier collaboration. Despite promising progress, there are some critical challenges related to classifying images in digital pathology, such as huge input sizes (e.g., gigapixels) and expensive processing time. Most of the existing models for classification of histopathology images are very large and accordingly have many parameters to be learned/optimized. In addition, due to the large size of Whole Slide Images (WSIs), e.g., 100,000 × 100,000 pixels, models require enormous computational times to achieve reliable results. In order to address these challenges, we propose a more compact network which is customized to classify cancer subtypes with lower computation time and memory complexity. This model is based on EfficientNet topology, but it is tailored for classifying histopathology images. The utilized model is evaluated over three tumor types brain, lung, and kidney from The Cancer Genome Atlas (TCGA) public repository. Since the pre-trained EfficientNet works properly with the specific size of images, an effective approach is proposed to adjust the size of input images. The proposed model can be trained with a much smaller training set for applications such as image search that require robust and compact representations. The results show that the proposed model, compared to state-of-the-art models, i.e., KimiaNet, can classify cancer subtypes more accurately and provides superior results. In addition, the proposed model achieves memory and computational efficiency in the training phase and is a more compact deep topology compared to KimiaNet. © 2022 IEEE.

Evaluation of EfficientNet models for COVID-19 detection using lung parenchyma.

When the COVID-19 pandemic broke out in the beginning of 2020, it became crucial to enhance early diagnosis with efficient means to reduce dangers and future spread of the viruses as soon as possible. Finding effective treatments and lowering mortality rates is now more important than ever. Scanning with a computer tomography (CT) scanner is a helpful method for detecting COVID-19 in this regard. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19-positive and 86 COVID-19-negative patients taken at the Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies show that the modified EfficientNet-ap-nish method uses this dataset effectively for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a preprocessing stage. Then, performance pretrained models are analyzed using different CNN architectures and with our Nish activation function. The statistical rates are obtained by the various EfficientNet models and the highest detection score is obtained with the EfficientNet-B4-ap-nish version, which provides a 97.93% accuracy rate and a 97.33% F1-score. The implications of the proposed method are immense both for present-day applications and future developments.

Deep efficient-nets with transfer learning assisted detection of COVID-19 using chest X-ray radiology imaging.

Corona Virus (COVID-19) could be considered as one of the most devastating pandemics of the twenty-first century. The effective and the rapid screening of infected patients could reduce the mortality and even the contagion rate. Chest X-ray radiology could be designed as one of the effective screening techniques for COVID-19 exploration. In this paper, we propose an advanced approach based on deep learning architecture to automatic and effective screening techniques dedicated to the COVID-19 exploration through chest X-ray (CXR) imaging. Despite the success of state-of-the-art deep learning-based models for COVID-19 detection, they might suffer from several problems such as the huge memory and the computational requirement, the overfitting effect, and the high variance. To alleviate these issues, we investigate the Transfer Learning to the Efficient-Nets models. Next, we fine-tuned the whole network to select the optimal hyperparameters. Furthermore, in the preprocessing step, we consider an intensity-normalization method succeeded by some data augmentation techniques to solve the imbalanced dataset classes' issues. The proposed approach has presented a good performance in detecting patients attained by COVID-19 achieving an accuracy rate of 99.0% and 98% respectively using training and testing datasets. A comparative study over a publicly available dataset with the recently published deep-learning-based architectures could attest the proposed approach's performance.

An Improved EfficientNet Model and its Applications in Pneumonia Image Classification

Since the outburst of COVID-19, the medical system has been facing great challenges due to the explosive growth in detection and treatment needs within a short period. To improve the working efficiency of doctors, an improved EfficientNet model of Convolutional Neural Network (CNN) was proposed and applied for the diagnosis of pneumonia cases and the classification of relevant images in the present study. First, the acquired images of pneumonia cases were divided to determine the zones with target features, and image size was limited to improve the training speed of the network. Meanwhile, reinforcement learning was performed to the input dataset to further improve the training effect of the model. Second, the preprocessed images were inputted into the improved EfficientNet-B4 model. The depth and width of the model, as well as the resolution of the input images, were determined by optimizing the combination coefficient. On this basis, the model was scaled, and then its ability in extracting the features of deep-layer images was strengthened by introducing the attention mechanism. Third, the learning rate was adjusted by using the Adaptive Momentum (ADAM), and the training efficiency of the model was accelerated. Finally, the test set was inputted into the trained model. Results demonstrate that the improved model could detect 98% of patients with pneumonia and 97% of patients without pneumonia. The accuracy rate, precision rate, and sensitivity of the model were generally improved. Lastly, the training and test results of VGGNet, SqueezeNet-Elus, SqueezeNet-Relu, and the improved EfficientNet-B4 models were compared and evaluated. The improved EfficientNet-B4 model achieved the highest comprehensive accuracy rate, reaching 92.95%. The proposed method provides some references to the application of the CNN model in image classification and recognition. © 2022 School of Science, IHU. All Rights Reserved.

Covid-19 Detection Based on Lung Lesion Signs in Big Data Processing Environment

The world is going through a global health crisis known as the Covid-19 pandemic. Currently, the outbreak is still evolving in a complicated way with a high spreading speed and new variants appearing constantly. RT-PCR test is preferred to test a patient infected with Covid-19. However, this method depends on many factors such as the time of specimen collection and preservation procedure. The cost to perform the RT-PCR test is quite high and requires a system of specialized machinery for sample analysis. Using deep learning techniques on medial images provides promising results with high accuracy with recent technological advancements. In this study, we propose a deep learning method based on CasCade R-CNN ResNet-101 and CasCade R-CNN EfficientNet in a big data processing environment that accelerates the detection of Covid-19 infections on chest X-rays. Chest X-ray can quickly be performed in most medical facilities and provides important information in detecting suspected Covid-19 cases in an inexpensive way. Experimental results show that the classification of lung lesions infected with Covid-19 has an accuracy of 96% and mAP of 99%. This method effectively supports doctors to have more basis to identify patients infected with Covid-19 for timely treatment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Hybrid Detection Model for Meticulous Presaging of Heart Disease using Deep Learning: HDMPHD

Heart diseases that occur due to the blockage of coronary arteries, which causes heart attack, are also commonly known as myocardial infarction. Rapid detection and acute diagnosis of myocardial infarction avoid death. The electrocardiographic test or ECG signals are used to diagnosis myocardial infarction with the help of ST variations in the heart rhythm. ECG helps to detect whether the patient is normal and suffering from myocardial infarction. In blood, when the enzyme value increases, after a certain time pass occurs, heart attack. For ECG images, the manual reviewing process is a very difficult task. Due to advancements in technology, computer-aided tools and software are used to diagnosis myocardial infarction,because manual ECG requires more expertise .so that automatic detection of myocardial infarction on ECG could be done by different machine learning tools. This study detects the normal and myocardial infarction patients by selecting the feature with their feature weights by selecting from the model and by Random forest classifier selecting the index value using DenseNet-121, ResNet_50, and EfficientNet_b0 deep learning techniques .This proposed work used the real dataset from Medanta hospital (India) at the time of covid 19. The dataset is in the form of ECG images for Normal and myocardial infarction (960 samples). With an end-to-end structure, deep learning implements the standard 12-lead ECG signals for the detection of normal and myocardial infarction..The proposed model provides high performance on normal and myocardial infarction detection. The accuracy achieved by the proposed model for Efficientnet_b0 Random Forest to Select from Model Accuracy 84.244792, Precision 84.396532, Recall 84.227410, F-Measure 84.222295. © The Author(s) 2022.

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available.

Image Segmentation For pneumothorax disease Based On based on Nested Unet Model

Pneumothorax on lung can be caused by a blunt chest injury, damage from underlying lung disease or Covid-19 Virus. Using CT scanning to examine high-risk people is an important task for many doctors and hospital. With the development of machine learning techniques, computer-aided diagnosis is widely used in pneumothorax detection. In this paper, we proposed a nested Unet model with a backbone of EfficientNet. This model used many skip pathway connections in many layers to reduce the semantic gap between networks. We choose dice loss as our experiment metrics, which is widely used in segmentation task. The lower Dice loss is, the better performance the model has. Compared with the simple Unet model or the other models, the experiments show that our model has better performance. © 2022 IEEE.

Identification and Localization of COVID-19 Abnormalities on Chest Radiographs using Ensembled Deep Neural Networks

With the hit of the global pandemic COVID-19, the chest X-ray domain has gained prominence. It has been recognised as one of the principal methods to learn the presence of infection and its effect on various internal organs like the lungs. Chest radiographs show abnormalities due to COVID-19 that appear similar to the anomalies caused by other viruses and bacteria, thus making it challenging for technicians to detect. Therefore, it becomes almost inevitable to have a computer vision model that identifies and localizes the COVID-19 virus to help doctors provide an immediate and confident diagnosis. The models in computer vision tasks have seen considerable advancements in deep learning, so the proposed model tried to integrate a few of them to come up with a model for classifying and localising the diagnosis of COVID-19 using chest X-rays. This paper ensembles a few state-of-the-art models in classification and object detection to build a model for chest radiograph diagnosis. The proposed ensembled model is found to achieve the mean Average Precision value of 0.627 on SIIM-FISABIO-RSNA COVID-19 dataset. © 2022 IEEE.

EfficientMask-Net for face authentication in the era of COVID-19 pandemic.

Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobile and embedding models with low-memory requirements. The method identifies face mask-wearing conditions in two different schemes: I. Correctly Face Mask (CFM), Incorrectly Face Mask (IFM), and Not Face Mask (NFM) wearing; II. Uncovered Chin IFM, Uncovered Nose IFM, and Uncovered Nose and Mouth IFM. The proposed method can also be helpful to unmask the face for face authentication based on unconstrained 2D facial images in the wild. In this study, deep convolutional neural networks (CNNs) were employed as feature extractors. Then, deep features were fed to a recently proposed large margin piecewise linear (LMPL) classifier. In the experimental study, lightweight and very powerful mobile implementation of CNN models were evaluated, where the novel "EffientNetb0" deep feature extractor with LMPL classifier outperformed well-known end-to-end CNN models, as well as conventional image classification methods. It achieved high accuracies of 99.53 and 99.64% in fulfilling the two mentioned tasks, respectively.

U-Net-based COVID-19 CT Image Semantic Segmentation: A Transfer Learning Approach

Deep learning (DL) algorithms are widely applied in many disciplines such as medical imaging, bioinformatics, and computer vision. DL models have been used in medical imaging to perform image segmentation, classification, and detection. During the outbreak of the COVID-19 pandemic, DL has been extensively used to develop COVID-19 screening systems. The reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard method for COVID-19 screening. However, DL has been proposed to detect patients infected with COVID-19 through radiological imaging in Chest X-rays and chest computed tomography (CT) images. This paper proposes transfer learning to train modified U-Net models to segment the COVID-19 chest CT images into two regions of lung infection (ground-glass and consolidation). The proposed modified U-Net models were constructed by replacing the encoder part with a pre-trained convolutional neural network (CNN) model. Three pre-trained CNN models, namely, EfficientNet-b0, EfficientNet-b1, and EfficientNet-b2 were used. The proposed models were evaluated on the COVID-19 CT Images Segmentation dataset available in an open Kaggle challenge. The obtained results show that the proposed EfficientNet-b2_U-Net model yielded the highest FScore of 0.5666. © 2022 IEEE.

Covid-19 Detection in Chest X-ray Images with Deep Learning

One of the primary methods to diagnose Covid-19 illness is to examine Chest X-ray images. In most patients, these images contain abnormalities caused by Covid-19 viral pneumonia. In this study, we conducted extensive empirical analysis to detect such pneumonia on images using Convolutional Neural Networks. Our analysis on a set of existing CNN models show that some of these models are insufficient in decision making. In this context, various binary class classification models are trained using the Covid-19 data from COVID-CXNet dataset and Normal class data from the NIH Chest X-ray dataset. We used Contrast Limited Adaptive Histogram Equalization (CHALE) and Bi-Histogram Equalization (BEASF) based on Adaptive Sigmoid Function for preprocessing the data. Using the transfer learning techniques, ImageNet pretrained models of various CNN models, i.e. DenseNet, VGGNet, EfficientNet are adapted to this domain. The best result is obtained, with a 0.99 F1 score, using the EfficientNetB5 model with preprocessed data.

COVID-19 localization and recognition on chest radiographs based on Yolov5 and EfficientNet

Chest radiographs clearly present the characteristics of lung lesions in patients with new coronary pneumonia, thus they can be leveraged to build a new coronary pneumonia detection model to provide doctors with favorable auxiliary diagnosis results. This paper proposes a COVID-19 localization and identification approach based on yolov5 and EfficientNet. Due to inherent reasons such as computational complexity and network structure, the features of a single model are usually limited in representation, and EfficientNet provides yolov5 with competitive feature expression through BiFPN and other advantages, and the ensemble of EfficientNet and yolov5 recognition results will significantly improve the performance of a single model. In order to evaluate the robustness of the approach proposed in this paper, we trained our network on COVID-19 Detection datasets from Kaggle platform. Evaluations and comparisons demonstrate that the ensemble approach achieves better performance on various scenes. © 2022 IEEE.

Identification of COVID-19 and Pneumonia in X-Ray Images using Transfer Learning

Background: The spread of novel corona virus disease (COVID-19) contributed to a global crisis in the world. The virus was identified a pandemic by the World Health Organization on 11 March 20. And also more than 200 countries have been affected by the outbreak, with more than 37 million cases reported and over 1 million fatalities as of 10 October 2020. The SARS-CoV-2 virus triggered the COVID-19 disease. The human respiratory system deteriorates from this severe disease. In the first four to ten days after infection, patients with COVID-19 may develop symptoms similar to pneumonia as well as other respiratory diseases. As a reason, there could be a misdiagnosis between COVID-19 and common pneumonia patients. So for that, Computer-Aided Diagnosis (CAD) system can be considered an effective technique for physicians to use medical imaging methods to help the detection of pneumonia and COVID-19. Method: Some methods of deep learning can assist doctors in achieving an accurate pre-diagnosis. This paper proposed a specific and precise method for classification of pneumonia, COVID- 19 and healthy patients using x-ray images, even in the case of a small number of labeled samples being available. EfficientNet-B4 is used to train & predict precise result it is a transfer learning approach. To bring the proposed model to reality, additional layers are added to the EfficientNet-B4 model. Results: The proposed work has procured a high training accuracy of 99% by 3886 (1200:COVID-19, 1345: pneumonia and 1341: healthy) X-ray images. And it accurately classifies the classes of Covid 19, Pneumonia and Healthy images with testing accuracy 99%, 93% and 100% respectively. Conclusion: Due to a high spreading of corona- virus, the identification of COVID-19 in early age plays an important role in implementing preventive steps. The developed model will accurately identify The diseases called COVID-19 and Pneumonia in X- Ray images at early stages. The obtained results specify that the proposed work attained best results compared with the previous approaches. © 2022 IEEE.

ECOVIDNET: Snapshot Ensembling Approach to Detect Coronavirus from Chest X-ray Images

To tackle the global pandemic of COVID-19, scholars are looking for accurate and efficient artificial intelligence approaches to screen the chest situation of the X-Ray images of the COVID-Affected people. Developing an accurate deep model is a goal which can be achieved through an ensemble of multiple deep models. Utilizing multiple networks boosts the performance and surpasses utilizing a single model classifier. However, it suffers from a high computational cost of training. To avoid this, we propose a novel deep network model namely ECOVIDNet. The proposed model is based on merging multiple model snapshots for final prediction at the cost of a single training run. The proposed scheme adopts EfficientNet through the transfer learning process with freezing all trainable layers and adding two fully connected layers at the end of the model. The model is trained on an X-ray image dataset with achieving an accuracy of 99.2%, 96.8% for binary (Normal vs COVID-19), and ternary (Normal vs COVID-19 vs Pneumonia) classifications. The model is evaluated with 5-fold cross-validation and obtained precision, sensitivity, and F1-score of 99.5%, 99.5, and 99.4%, respectively. Also, the proposed model yields 96.62% of precision, 96.5% of sensitivity, and 96.48% of F1-score in ternary classification. © 2022 IEEE.