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Fu DS, Huang J, Hazra D, Dwivedi AK, Gupta SK, Shivahare BD, Garg D. Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture. PLoS One 2024; 19:e0303462. [PMID: 38990969 PMCID: PMC11239052 DOI: 10.1371/journal.pone.0303462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/25/2024] [Indexed: 07/13/2024] Open
Abstract
Nowadays, federated learning is one of the most prominent choices for making decisions. A significant benefit of federated learning is that, unlike deep learning, it is not necessary to share data samples with the model owner. The weight of the global model in traditional federated learning is created by averaging the weights of all clients or sites. In the proposed work, a novel method has been discussed to generate an optimized base model without hampering its performance, which is based on a genetic algorithm. Chromosome representation, crossover, and mutation-all the intermediate operations of the genetic algorithm have been illustrated with useful examples. After applying the genetic algorithm, there is a significant improvement in inference time and a huge reduction in storage space. Therefore, the model can be easily deployed on resource-constrained devices. For the experimental work, sports data has been used in balanced and unbalanced scenarios with various numbers of clients in a federated learning environment. In addition, we have used four famous deep learning architectures, such as AlexNet, VGG19, ResNet50, and EfficientNetB3, as the base model. We have achieved 92.34% accuracy with 9 clients in the balanced data set by using EfficientNetB3 as the base model using a GA-based approach. Moreover, after applying the genetic algorithm to optimize EfficientNetB3, there is an improvement in inference time and storage space by 20% and 2.35%, respectively.
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Affiliation(s)
- De Sheng Fu
- College of Public Education, ZheJiang Institute of Economics and Trade HangZhou, ZheJiang, China
| | - Jie Huang
- College of Business administration ZheJiang Institute of Economics and Trade HangZhou, ZheJiang, China
| | - Dibyanarayan Hazra
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India
| | - Amit Kumar Dwivedi
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India
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2
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Hroub NA, Alsannaa AN, Alowaifeer M, Alfarraj M, Okafor E. Explainable deep learning diagnostic system for prediction of lung disease from medical images. Comput Biol Med 2024; 170:108012. [PMID: 38262202 DOI: 10.1016/j.compbiomed.2024.108012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/26/2023] [Accepted: 01/17/2024] [Indexed: 01/25/2024]
Abstract
Around the globe, respiratory lung diseases pose a severe threat to human survival. Based on a central goal to reduce contiguous transmission from infected to healthy persons, several technologies have evolved for diagnosing lung pathologies. One of the emerging technologies is the utility of Artificial Intelligence (AI) based on computer vision for processing wide varieties of medical imaging but AI methods without explainability are often treated as a black box. Based on a view to demystifying the rationale influencing AI decisions, this paper designed and developed a novel low-cost explainable deep-learning diagnostic tool for predicting lung disease from medical images. For this, we investigated explainable deep learning (DL) models (conventional DL and vision transformers (ViTs)) for performing prediction of the existence of pneumonia, COVID19, or no-disease from both original and data augmentation (DA)-based medical images (from two chest X-ray datasets). The results show that our experimental consideration of the DA that combines the impact of cropping, rotation, and horizontal flipping (CROP+ROT+HF) for transforming input images and then passed as input to an Inception-V3 architecture yielded a performance that surpasses all the ViTs and other conventional DL approaches in most of the evaluated performance metrics. Overall, the results suggest that the utility of data augmentation schemes aided the DL methods to yield higher classification accuracies. Furthermore, we compared five different class activation mapping (CAM) algorithms (GradCAM, GradCAM++, EigenGradCAM, AblationCAM, and RandomCAM). The result shows that most of the examined CAM algorithms were effective in identifying the attention region containing the existence of pneumonia or COVID-19 from the medical images (chest X-rays). Our developed low-cost AI diagnostic tool (pilot system) can assist medical experts and radiographers in proffering early diagnosis of lung disease. For this, we selected five to seven deep learning models and the explainable algorithms were deployed on a novel web interface implemented via a Gradio framework.
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Affiliation(s)
- Nussair Adel Hroub
- SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia
| | - Ali Nader Alsannaa
- SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia
| | - Maad Alowaifeer
- SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia; Electrical Engineering Department, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia
| | - Motaz Alfarraj
- SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia; Electrical Engineering Department, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia; Information and Computer Science Department, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia
| | - Emmanuel Okafor
- SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia.
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3
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Hussein AM, Sharifai AG, Alia OM, Abualigah L, Almotairi KH, Abujayyab SKM, Gandomi AH. Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs. Sci Rep 2024; 14:534. [PMID: 38177156 PMCID: PMC10766625 DOI: 10.1038/s41598-023-47038-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/08/2023] [Indexed: 01/06/2024] Open
Abstract
The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.
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Affiliation(s)
- Ahmad MohdAziz Hussein
- Department of Computer Science, Faculty of Information Technology, Middle East University, Amman, Jordan.
| | - Abdulrauf Garba Sharifai
- Department of Computer Sciences, Yusuf Maitama Sule University, Kofar Nassarawa, Kano, 700222, Nigeria
| | - Osama Moh'd Alia
- Department of Computer Science, Faculty of Computes and Information Technology, University of Tabuk, 71491, Tabuk, Saudi Arabia
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
- School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia
- School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Khaled H Almotairi
- Computer Engineering Department, Computer and Information Systems College, Umm Al-Qura University, 21955, Makkah, Saudi Arabia
| | | | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary.
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4
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Otsuka Y, Indo H, Kawashima Y, Tanaka T, Kono H, Kikuchi M. Eichner classification based on panoramic X-ray images using deep learning: A pilot study. Biomed Mater Eng 2024; 35:377-386. [PMID: 38848165 DOI: 10.3233/bme-230217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
BACKGROUND Research using panoramic X-ray images using deep learning has been progressing in recent years. There is a need to propose methods that can classify and predict from image information. OBJECTIVE In this study, Eichner classification was performed on image processing based on panoramic X-ray images. The Eichner classification was based on the remaining teeth, with the aim of making partial dentures. This classification was based on the condition that the occlusal position was supported by the remaining teeth in the upper and lower jaws. METHODS Classification models were constructed using two convolutional neural network methods: the sequential and VGG19 models. The accuracy was compared with the accuracy of Eichner classification using the sequential and VGG19 models. RESULTS Both accuracies were greater than 81%, and they had sufficient functions for the Eichner classification. CONCLUSION We were able to build a highly accurate prediction model using deep learning scratch sequential model and VGG19. This predictive model will become part of the basic considerations for future AI research in dentistry.
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Affiliation(s)
- Yuta Otsuka
- Department of Biomaterials Science, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Hiroko Indo
- Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Yusuke Kawashima
- Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Tatsuro Tanaka
- Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Hiroshi Kono
- Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Masafumi Kikuchi
- Department of Biomaterials Science, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
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Rocha J, Pereira SC, Pedrosa J, Campilho A, Mendonça AM. STERN: Attention-driven Spatial Transformer Network for abnormality detection in chest X-ray images. Artif Intell Med 2024; 147:102737. [PMID: 38184361 DOI: 10.1016/j.artmed.2023.102737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 11/16/2023] [Accepted: 11/28/2023] [Indexed: 01/08/2024]
Abstract
Chest X-ray scans are frequently requested to detect the presence of abnormalities, due to their low-cost and non-invasive nature. The interpretation of these images can be automated to prioritize more urgent exams through deep learning models, but the presence of image artifacts, e.g. lettering, often generates a harmful bias in the classifiers and an increase of false positive results. Consequently, healthcare would benefit from a system that selects the thoracic region of interest prior to deciding whether an image is possibly pathologic. The current work tackles this binary classification exercise, in which an image is either normal or abnormal, using an attention-driven and spatially unsupervised Spatial Transformer Network (STERN), that takes advantage of a novel domain-specific loss to better frame the region of interest. Unlike the state of the art, in which this type of networks is usually employed for image alignment, this work proposes a spatial transformer module that is used specifically for attention, as an alternative to the standard object detection models that typically precede the classifier to crop out the region of interest. In sum, the proposed end-to-end architecture dynamically scales and aligns the input images to maximize the classifier's performance, by selecting the thorax with translation and non-isotropic scaling transformations, and thus eliminating artifacts. Additionally, this paper provides an extensive and objective analysis of the selected regions of interest, by proposing a set of mathematical evaluation metrics. The results indicate that the STERN achieves similar results to using YOLO-cropped images, with reduced computational cost and without the need for localization labels. More specifically, the system is able to distinguish abnormal frontal images from the CheXpert dataset, with a mean AUC of 85.67% - a 2.55% improvement vs. the 0.98% improvement achieved by the YOLO-based counterpart in comparison to a standard baseline classifier. At the same time, the STERN approach requires less than 2/3 of the training parameters, while increasing the inference time per batch in less than 2 ms. Code available via GitHub.
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Affiliation(s)
- Joana Rocha
- INESC TEC and Faculty of Engineering, University of Porto, R. Dr. Roberto Frias s/n, 4200-465, Porto, Portugal.
| | - Sofia Cardoso Pereira
- INESC TEC and Faculty of Engineering, University of Porto, R. Dr. Roberto Frias s/n, 4200-465, Porto, Portugal
| | - João Pedrosa
- INESC TEC and Faculty of Engineering, University of Porto, R. Dr. Roberto Frias s/n, 4200-465, Porto, Portugal
| | - Aurélio Campilho
- INESC TEC and Faculty of Engineering, University of Porto, R. Dr. Roberto Frias s/n, 4200-465, Porto, Portugal
| | - Ana Maria Mendonça
- INESC TEC and Faculty of Engineering, University of Porto, R. Dr. Roberto Frias s/n, 4200-465, Porto, Portugal
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6
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Moitra M, Alafeef M, Narasimhan A, Kakaria V, Moitra P, Pan D. Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images. PLoS One 2023; 18:e0290494. [PMID: 38096254 PMCID: PMC10721010 DOI: 10.1371/journal.pone.0290494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/09/2023] [Indexed: 12/17/2023] Open
Abstract
COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (CT) imaging has remained the first line of diagnosis for individuals infected with SARS-CoV-2. However, differentiating COVID-19 from other types of pneumonia and predicting associated cardiovascular complications from the same chest-CT images have remained challenging. In this study, we have first used transfer learning method to distinguish COVID-19 from other pneumonia and healthy cases with 99.2% accuracy. Next, we have developed another CNN-based deep learning approach to automatically predict the risk of cardiovascular disease (CVD) in COVID-19 patients compared to the normal subjects with 97.97% accuracy. Our model was further validated against cardiac CT-based markers including cardiac thoracic ratio (CTR), pulmonary artery to aorta ratio (PA/A), and presence of calcified plaque. Thus, we successfully demonstrate that CT-based deep learning algorithms can be employed as a dual screening diagnostic tool to diagnose COVID-19 and differentiate it from other pneumonia, and also predicts CVD risk associated with COVID-19 infection.
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Affiliation(s)
- Moumita Moitra
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America
| | - Maha Alafeef
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America
- Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
- Department of Nuclear Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Arjun Narasimhan
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
| | - Vikram Kakaria
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
| | - Parikshit Moitra
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
- Department of Nuclear Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Dipanjan Pan
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America
- Department of Nuclear Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
- Department of Materials Science & Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
- Huck Institutes of the Life Sciences, State College, Pennsylvania, United States of America
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7
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Umapathy S, Murugappan M, Bharathi D, Thakur M. Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques. Diagnostics (Basel) 2023; 13:2987. [PMID: 37761354 PMCID: PMC10527774 DOI: 10.3390/diagnostics13182987] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/03/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Diagnosing Intracranial Hemorrhage (ICH) at an early stage is difficult since it affects the blood vessels in the brain, often resulting in death. We propose an ensemble of Convolutional Neural Networks (CNNs) combining Squeeze and Excitation-based Residual Networks with the next dimension (SE-ResNeXT) and Long Short-Term Memory (LSTM) Networks in order to address this issue. This research work primarily used data from the Radiological Society of North America (RSNA) brain CT hemorrhage challenge dataset and the CQ500 dataset. Preprocessing and data augmentation are performed using the windowing technique in the proposed work. The ICH is then classified using ensembled CNN techniques after being preprocessed, followed by feature extraction in an automatic manner. ICH is classified into the following five types: epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural. A gradient-weighted Class Activation Mapping method (Grad-CAM) is used for identifying the region of interest in an ICH image. A number of performance measures are used to compare the experimental results with various state-of-the-art algorithms. By achieving 99.79% accuracy with an F-score of 0.97, the proposed model proved its efficacy in detecting ICH compared to other deep learning models. The proposed ensembled model can classify epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural hemorrhages with an accuracy of 99.89%, 99.65%, 98%, 99.75%, and 99.88%. Simulation results indicate that the suggested approach can categorize a variety of intracranial bleeding types. By implementing the ensemble deep learning technique using the SE-ResNeXT and LSTM models, we achieved significant classification accuracy and AUC scores.
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Affiliation(s)
- Snekhalatha Umapathy
- Department of Biomedical Engineering, SRM Institute of Science and Technology, Chennai 603203, India
- College of Engineering, Architecture, and Fine Arts, Batangas State University, Batangas 4200, Philippines
| | - Murugappan Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait
- Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, India
- Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Deepa Bharathi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai 600089, India
| | - Mahima Thakur
- Department of Biomedical Engineering, SRM Institute of Science and Technology, Chennai 603203, India
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8
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Ghnemat R, Alodibat S, Abu Al-Haija Q. Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification. J Imaging 2023; 9:177. [PMID: 37754941 PMCID: PMC10532018 DOI: 10.3390/jimaging9090177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 08/19/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneumonia), COVID-19 Image Dataset (COVID-19, Viral Pneumonia, Normal), and COVID-19 Radiography Database. We achieved testing and validation accuracy of 90.6% on a relatively small dataset of 6432 images. Our proposed model improved accuracy and reduced time complexity, making it more practical for medical diagnosis. Our approach offers a more interpretable and transparent AI model that can enhance the accuracy and efficiency of medical diagnosis.
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Affiliation(s)
- Rawan Ghnemat
- Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, Jordan
| | - Sawsan Alodibat
- Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, Jordan
| | - Qasem Abu Al-Haija
- Department of Cybersecurity, Princess Sumaya University for Technology, Amman 11941, Jordan
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9
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Mozaffari J, Amirkhani A, Shokouhi SB. A survey on deep learning models for detection of COVID-19. Neural Comput Appl 2023; 35:1-29. [PMID: 37362568 PMCID: PMC10224665 DOI: 10.1007/s00521-023-08683-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-023-08683-x.
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Affiliation(s)
- Javad Mozaffari
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Abdollah Amirkhani
- School of Automotive Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Shahriar B. Shokouhi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
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10
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Subramanian M, Sathishkumar VE, Cho J, Shanmugavadivel K. Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images. Sci Rep 2023; 13:8516. [PMID: 37231044 DOI: 10.1038/s41598-023-34908-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 05/09/2023] [Indexed: 05/27/2023] Open
Abstract
COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor COVID-19, especially in severe cases. But, visual inspection of CT images takes time and effort. In this study, we employ Convolution Neural Network (CNN) to detect coronavirus infection from CT images. The proposed study utilized transfer learning on the three pre-trained deep CNN models, namely VGG-16, ResNet, and wide ResNet, to diagnose and detect COVID-19 infection from the CT images. However, when the pre-trained models are retrained, the model suffers the generalization capability to categorize the data in the original datasets. The novel aspect of this work is the integration of deep CNN architectures with Learning without Forgetting (LwF) to enhance the model's generalization capabilities on both trained and new data samples. The LwF makes the network use its learning capabilities in training on the new dataset while preserving the original competencies. The deep CNN models with the LwF model are evaluated on original images and CT scans of individuals infected with Delta-variant of the SARS-CoV-2 virus. The experimental results show that of the three fine-tuned CNN models with the LwF method, the wide ResNet model's performance is superior and effective in classifying original and delta-variant datasets with an accuracy of 93.08% and 92.32%, respectively.
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Affiliation(s)
- Malliga Subramanian
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India
| | | | - Jaehyuk Cho
- Department of Software Engineering, Jeonbuk National University, Jeongu-si, Republic of Korea.
| | - Kogilavani Shanmugavadivel
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India
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11
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Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression. Life (Basel) 2023; 13:life13030691. [PMID: 36983845 PMCID: PMC10056696 DOI: 10.3390/life13030691] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare.
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12
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Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer Diagnosis. Diagnostics (Basel) 2023; 13:diagnostics13040686. [PMID: 36832174 PMCID: PMC9955324 DOI: 10.3390/diagnostics13040686] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 01/14/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
Cervical cancer is one of the most common types of cancer among women, which has higher death-rate than many other cancer types. The most common way to diagnose cervical cancer is to analyze images of cervical cells, which is performed using Pap smear imaging test. Early and accurate diagnosis can save the lives of many patients and increase the chance of success of treatment methods. Until now, various methods have been proposed to diagnose cervical cancer based on the analysis of Pap smear images. Most of the existing methods can be divided into two groups of methods based on deep learning techniques or machine learning algorithms. In this study, a combination method is presented, whose overall structure is based on a machine learning strategy, where the feature extraction stage is completely separate from the classification stage. However, in the feature extraction stage, deep networks are used. In this paper, a multi-layer perceptron (MLP) neural network fed with deep features is presented. The number of hidden layer neurons is tuned based on four innovative ideas. Additionally, ResNet-34, ResNet-50 and VGG-19 deep networks have been used to feed MLP. In the presented method, the layers related to the classification phase are removed in these two CNN networks, and the outputs feed the MLP after passing through a flatten layer. In order to improve performance, both CNNs are trained on related images using the Adam optimizer. The proposed method has been evaluated on the Herlev benchmark database and has provided 99.23 percent accuracy for the two-classes case and 97.65 percent accuracy for the 7-classes case. The results have shown that the presented method has provided higher accuracy than the baseline networks and many existing methods.
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Gulakala R, Markert B, Stoffel M. Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107262. [PMID: 36463675 PMCID: PMC9699959 DOI: 10.1016/j.cmpb.2022.107262] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 11/04/2022] [Accepted: 11/22/2022] [Indexed: 05/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Covid-19 infections are spreading around the globe since December 2019. Several diagnostic methods were developed based on biological investigations and the success of each method depends on the accuracy of identifying Covid infections. However, access to diagnostic tools can be limited, depending on geographic region and the diagnosis duration plays an important role in treating Covid-19. Since the virus causes pneumonia, its presence can also be detected using medical imaging by Radiologists. Hospitals with X-ray capabilities are widely distributed all over the world, so a method for diagnosing Covid-19 from chest X-rays would present itself. Studies have shown promising results in automatically detecting Covid-19 from medical images using supervised Artificial neural network (ANN) algorithms. The major drawback of supervised learning algorithms is that they require huge amounts of data to train. Also, the radiology equipment is not computationally efficient for deep neural networks. Therefore, we aim to develop a Generative Adversarial Network (GAN) based image augmentation to optimize the performance of custom, light, Convolutional networks used for the classification of Chest X-rays (CXR). METHODS A Progressively Growing Generative Adversarial Network (PGGAN) is used to generate synthetic and augmented data to supplement the dataset. We propose two novel CNN architectures to perform the Multi-class classification of Covid-19, healthy and pneumonia affected Chest X-rays. Comparisons have been drawn to the state of the art models and transfer learning methods to evaluate the superiority of the networks. All the models are trained using enhanced and augmented X-ray images and are compared based on classification metrics. RESULTS The proposed models had extremely high classification metrics with proposed Architectures having test accuracy of 98.78% and 99.2% respectively while having 40% lesser training parameters than their state of the art counterpart. CONCLUSION In the present study, a method based on artificial intelligence is proposed, leading to a rapid diagnostic tool for Covid infections based on Generative Adversarial Network (GAN) and Convolutional Neural Networks (CNN). The benefit will be a high accuracy of detection with up to 99% hit rate, a rapid diagnosis, and an accessible Covid identification method by chest X-ray images.
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Affiliation(s)
- Rutwik Gulakala
- Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, D-52062 Aachen, Germany
| | - Bernd Markert
- Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, D-52062 Aachen, Germany
| | - Marcus Stoffel
- Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, D-52062 Aachen, Germany.
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Gulakala R, Markert B, Stoffel M. Generative adversarial network based data augmentation for CNN based detection of Covid-19. Sci Rep 2022; 12:19186. [PMID: 36357530 PMCID: PMC9647771 DOI: 10.1038/s41598-022-23692-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022] Open
Abstract
Covid-19 has been a global concern since 2019, crippling the world economy and health. Biological diagnostic tools have since been developed to identify the virus from bodily fluids and since the virus causes pneumonia, which results in lung inflammation, the presence of the virus can also be detected using medical imaging by expert radiologists. The success of each diagnostic method is measured by the hit rate for identifying Covid infections. However, the access for people to each diagnosis tool can be limited, depending on the geographic region and, since Covid treatment denotes a race against time, the diagnosis duration plays an important role. Hospitals with X-ray opportunities are widely distributed all over the world, so a method investigating lung X-ray images for possible Covid-19 infections would offer itself. Promising results have been achieved in the literature in automatically detecting the virus using medical images like CT scans and X-rays using supervised artificial neural network algorithms. One of the major drawbacks of supervised learning models is that they require enormous amounts of data to train, and generalize on new data. In this study, we develop a Swish activated, Instance and Batch normalized Residual U-Net GAN with dense blocks and skip connections to create synthetic and augmented data for training. The proposed GAN architecture, due to the presence of instance normalization and swish activation, can deal with the randomness of luminosity, that arises due to different sources of X-ray images better than the classical architecture and generate realistic-looking synthetic data. Also, the radiology equipment is not generally computationally efficient. They cannot efficiently run state-of-the-art deep neural networks such as DenseNet and ResNet effectively. Hence, we propose a novel CNN architecture that is 40% lighter and more accurate than state-of-the-art CNN networks. Multi-class classification of the three classes of chest X-rays (CXR), ie Covid-19, healthy and Pneumonia, is performed using the proposed model which had an extremely high test accuracy of 99.2% which has not been achieved in any previous studies in the literature. Based on the mentioned criteria for developing Corona infection diagnosis, in the present study, an Artificial Intelligence based method is proposed, resulting in a rapid diagnostic tool for Covid infections based on generative adversarial and convolutional neural networks. The benefit will be a high accuracy of lung infection identification with 99% accuracy. This could lead to a support tool that helps in rapid diagnosis, and an accessible Covid identification method using CXR images.
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Affiliation(s)
- Rutwik Gulakala
- grid.1957.a0000 0001 0728 696XInstitute of General Mechanics, RWTH Aachen University, Aachen, Germany
| | - Bernd Markert
- grid.1957.a0000 0001 0728 696XInstitute of General Mechanics, RWTH Aachen University, Aachen, Germany
| | - Marcus Stoffel
- grid.1957.a0000 0001 0728 696XInstitute of General Mechanics, RWTH Aachen University, Aachen, Germany
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Shibu George G, Raj Mishra P, Sinha P, Ranjan Prusty M. COVID-19 Detection on Chest X-Ray Images Using Homomorphic Transformation and VGG Inspired Deep Convolutional Neural Network. Biocybern Biomed Eng 2022; 43:1-16. [PMID: 36447948 PMCID: PMC9684127 DOI: 10.1016/j.bbe.2022.11.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/01/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification.
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Affiliation(s)
- Gerosh Shibu George
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Pratyush Raj Mishra
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Panav Sinha
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Manas Ranjan Prusty
- Centre for Cyber Physical Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
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Liu D, Liu J, Yuan P, Yu F. Lightweight prohibited item detection method based on YOLOV4 for x-ray security inspection. APPLIED OPTICS 2022; 61:8454-8461. [PMID: 36256160 DOI: 10.1364/ao.467717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
In the area of public safety and crime prevention, some research based on deep learning has achieved success in the detection of prohibited items for x-ray security inspection. However, the number of parameters and computational consumption of most object detection methods based on deep learning are huge, which makes the hardware requirements of these methods extremely high and limits their applications. In this paper, a lightweight prohibited item detection method based on YOLOV4 is proposed for x-ray security inspection. First, the MobilenetV3 is used to replace the backbone network of YOLOV4, and the depthwise separable convolution is used to optimize the neck and head of YOLOV4 to reduce the number of parameters and computational consumption. Second, an adaptive spatial-and-channel attention block is designed to optimize the neck of YOLOV4 in order to improve the feature extraction capability of our method and maintain the detection accuracy. Third, the focal loss is utilized to avoid the class imbalance problem during the training process. Finally, the method is evaluated on our real x-ray pseudocolor image dataset with YOLOV4 and YOLOV4-tiny. For the overall performance, the mean average precision of our method is 4.98% higher than YOLOV4-tiny and 0.07% lower than YOLOV4. The number of parameters and computational consumption of our method are slightly higher than YOLOV4-tiny and much lower than YOLOV4.
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Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7451551. [PMID: 36188684 PMCID: PMC9522509 DOI: 10.1155/2022/7451551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/07/2022] [Accepted: 07/28/2022] [Indexed: 01/10/2023]
Abstract
Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health challenges. The advancement of deep learning techniques and a large amount of data-enabled algorithms to outperform medical teams in certain imaging tasks, such as pneumonia detection, skin cancer classification, hemorrhage detection, and arrhythmia detection. Automated diagnostics, which are enabled by images extracted from patient examinations, allow for interesting experiments to be conducted. This research differs from the related studies that were investigated in the experiment. These works are capable of binary categorization into two categories. COVID-Net, for example, was able to identify a positive case of COVID-19 or a healthy person with 93.3% accuracy. Another example is CHeXNet, which has a 95% accuracy rate in detecting cases of pneumonia or a healthy state in a patient. Experiments revealed that the current study was more effective than the previous studies in detecting a greater number of categories and with a higher percentage of accuracy. The results obtained during the model's development were not only viable but also excellent, with an accuracy of nearly 96% when analyzing a chest X-ray with three possible diagnoses in the two experiments conducted.
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Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34:15313-15348. [PMID: 35702664 PMCID: PMC9186489 DOI: 10.1007/s00521-022-07424-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/29/2022]
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
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