Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

Mohammad‐Rahimi, Hossein; Nadimi, Mohadeseh; Ghalyanchi-Langeroudi, Azadeh; Taheri, Mohammad; Ghafouri‐Fard, Soudeh

doi:10.3389/fcvm.2021.638011

Cited by 84 publications

(50 citation statements)

References 119 publications

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“…Investigators have developed many models to detect COVID-19 during the past two years and have shown that there is a role for AI in detecting COVID-19 [ 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 277 , 278 , 279 , 280 , 281 ]. The 184 technical papers reviewed in this study provide up-to-date knowledge on the usage of AI techniques in detecting COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Role of Artificial Intelligence in COVID-19 Detection

Gudigar

Raghavendra

Nayak

et al. 2021

Sensors

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The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

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Section: Discussionmentioning

confidence: 99%

Role of Artificial Intelligence in COVID-19 Detection

Gudigar

Raghavendra

Nayak

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Also, the wide accessibility of CT scanners makes this task quicker. Adding to this, Machine learning (ML) and Deep learning (DL) methods are evolving rapidly that can lessen the workload of the medical experts by providing an automatic interpretation from a huge data sets [7] – [10] , [39] . Zhao et al [11] developed a transfer learned DenseNet model for the classification of CT scan images into COVID + ve and Normal categories.…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Kaur

Gandhi

Panigrahi

2021

IEEE J. Transl. Eng. Health Med.

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Background: Timely and precise identification of COVID-19 is an arduous task owing to the scarcity and inefficiency of the medical test kits. This has resulted in medical professionals turning towards Computed Tomography (CT) scans. Efforts are being made to design deep learning models capable of COVID-19 detection using CT scans. This has certainly reduced the manual intervention in disease detection but reported accuracy is limited. Methods: The present work proposes an automatic system for COVID-19 diagnosis based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier. Typically, features are extracted from the fully connected layer of transfer learned MobileNetv2 that are then employed for FKNN training. The hyper-parameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-arts. Conclusion: The outcomes presented in the paper will aid in speedy detection of this virus at the different phases. Such a detection system will also increase the opportunities for fast recovery of patients worldwide thereby releasing the pressure off the medical professionals and the healthcare system around the world.

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“…Techniques such as random rotations and translations of the training images were used by Alazab et al to artificially increase the size of their dataset from 98 to 1,000 training images and achieve competitive COVID-19 detection results [8]. Many papers report validation accuracies of over 95% [9].…”

Section: Introductionmentioning

confidence: 99%

LIME-Enabled Investigation of Convolutional Neural Network Performances in COVID-19 Chest X-Ray Detection

Cervantes

Chan

2021

2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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The Coronavirus Disease (COVID-19) has caused millions of casualties across the globe. One inexpensive and noninvasive screening method for COVID-19 is the analysis of chest X-ray (CXR) images for pathological features in the lungs. These features are difficult to detect by humans, but convolutional neural networks (CNN) have proven effective at extracting them. This paper uses four ImageNet-pre-trained CNNs: VGG16, DenseNet201, ResNet50, and EfficientNetB3 to perform transfer learning to a task of COVID-19 CXR image detection on a dataset containing COVID-19, healthy, and viral pneumonia CXR images. We compare the performance of the retrained CNNs using standard measures and investigate the features they use for their predictions using local interpretable model-agnostic explanations (LIME). The networks are retrained on two classification tasks: Task 1 consists of classifying healthy and COVID-19 CXR images and task 2 consists of classifying viral pneumonia and COVID-19 CXR images. We find that DenseNet201 and VGG16 achieve higher accuracies than ResNet50 and EfficientNetB3 in both tasks. However, the LIME explanations reveal that VGG16 does not learn disease-relevant features in the lungs, while DenseNet201, ResNet50, and EfficientNetB3 use regions in the lungs to make their predictions. This observation is reinforced by comparing LIME explanations with ground-truth lung regions on an unseen dataset. The prospect of using "black box" deep neural networks for automatic screening of CXRs for COVID-19 can be improved with LIME-enabled investigations of model performance.

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Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

Cited by 84 publications

References 119 publications

Role of Artificial Intelligence in COVID-19 Detection

Role of Artificial Intelligence in COVID-19 Detection

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

LIME-Enabled Investigation of Convolutional Neural Network Performances in COVID-19 Chest X-Ray Detection

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