xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

Abstract

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - . Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

A fuzzy fine-tuned model for COVID-19 diagnosis

“…In [69] a three-level fuzzy-tree is constructed, which acts like convolution. In [70] , a multi-stage transfer learning technique was used to deal with the problem of data scarcity. Since uncertainty and imprecision are integral to image data in datasets, some literature proposes fuzzy classifiers.…”

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images

“…In this work, the segmentation task was ignored, and readymade RBC cell images were used only to classify the malaria parasite. Conventional CNN models show imagespecific inductive bias [40], and they are based on a local receptive field. To capture global information, CNN models need larger kernels or very deep network models.…”

Deep Learning Algorithms for Automatic COVID-19 Detection on Chest X-Ray Images

“…The study managed to achieve accuracies of 92% and 98%, respectively [14]. Mondal et al obtained a 96% test accuracy when using the Vision Transformer for classifying chest X-ray images into the same three categories, namely COVID, pneumonia and normal lungs [15]. Park et al added a convolutional backbone for feature extraction, and developed a system to assess COVID severity.…”