Vision Transformer Based COVID-19 Detection Using Chest CT-scan images

Sahoo, Pranab; Saha, Sriparna; Mondal, Samrat; Gowda, Suraj

doi:10.1109/bhi56158.2022.9926823

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Their improved model, based on a simple CNN as a base layer, exhibits faster performance and comprehensively considers factors such as dataset size and quality, demonstrating significant advancements in COVID-19 classification. Sahoo et al [19] discuss a vision transformer-based COVID-19 screening approach using CT-scan datasets. The authors have shown that their suggested technique outperforms CNN-based SOTA algorithms in various measures, including accuracy, recall, and F1 score.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models

2024

Preprint

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Accurate diagnosis of COVID-19 is critical for patient management and disease control. In this study, we evaluate the performance of Convolutional Neural Network (CNN) models, including VGG, ResNet, Inception, and Xception, for COVID-19 detection using CT scans and chest X-ray images. Leveraging deep learning algorithms and multiple layers such as Conv2D, MaxPooling2D, Flatten, and Dense, we analyze medical images to identify COVID-19 patterns. Through comprehensive dataset training and evaluation, we assess model accuracy, sensitivity, and specificity. Our findings highlight the potential of CNN-based approaches for accurate COVID-19 diagnosis from chest radiography images, contributing to the development of advanced diagnostic tools in combating the pandemic.

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Section: Literature Surveymentioning

confidence: 99%

Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models

2024

Preprint

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“…In order to support clinical decisions, many Computer-Aided Diagnosis (CAD) systems have been developed. These systems are becoming increasingly common in various radiology contexts, including workflow optimization, abnormality detection, and disease progression monitoring 1 , 5 – 7 . Additionally, CAD-based solutions can offer doctors additional insights into the issue that might not be visible to the human eye.…”

Section: Introductionmentioning

confidence: 99%

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

Sahoo,

Sharma,

Saha

et al. 2024

Sci Rep

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Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection’s severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

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“…The authors in [12], introduced DeepCELL, specifically designed to classify cervical cytology images by learning feature representations through several kernels of different sizes, contributing to its effective image classification capabilities. Vision Transformer (ViT)-based approaches have also shown state-of-the-art (SOTA) results in the medical image classification problems [13], [14], [15], [16], [17]. The authors in [18] used ViT and DenseNet161 to classify cervical cytology images and attained 68% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Computer-Aided Cervical Cancer Detection Using a Novel Fuzzy Rank-Based Fusion

Sahoo,

Saha,

Mondal

et al. 2023

IEEE Access

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Cervical cancer is a severe and pervasive disease that poses a significant health threat to women globally. The Pap smear test is an efficient and effective method for detecting cervical cancer in its early stages. However, manual screening is labor-intensive and requires expert cytologists, leading to potential delays and inconsistencies in diagnosis. Deep Learning-based Computer-Aided Diagnosis (CAD) has shown significant results and can ease the problem of manual screening. However, one single model is sometimes insufficient to capture the complex data pattern for accurate disease prediction. In this work, we develop an end-to-end architecture utilizing three pre-trained models for the initial cervical cancer prediction. To aggregate the outcomes of these models, we propose a novel fuzzy rank-based ensemble considering two non-linear functions for the final level prediction. Unlike simple fusion techniques, the proposed architecture provides the final predictions on the test samples by considering the base classifier's confidence in the predictions. To further enhance the classification capabilities of these models, we integrate advanced augmentation techniques such as CutOut, MixUp, and CutMix. The proposed model is evaluated on two benchmark datasets, SIPaKMeD, and Mendeley LBC, using a 5-fold cross-validation approach. On the SIPaKMeD dataset, the proposed ensemble architecture achieves a classification accuracy of 97.18% and an F1-score of 97.16%. On the Mendeley LBC dataset, the accuracy reaches 99.22% with an F1-score of 99.19%. Experimental results demonstrate the proposed architecture's effectiveness and potential in cervical Pap smear image classification. This could aid medical professionals in making more informed treatment decisions, improving overall effectiveness in the testing process.

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Vision Transformer Based COVID-19 Detection Using Chest CT-scan images

Cited by 9 publications

References 12 publications

Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models

Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

Enhancing Computer-Aided Cervical Cancer Detection Using a Novel Fuzzy Rank-Based Fusion

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