Can I read A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with Transfer Learning on EtoBox?

A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with Transfer Learning by Rounaq, Sumera; Shah, Shahid Munir; Aljawarneh, Mahmoud is a scholarly article available to read on EtoBox.

What is A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with Transfer Learning about?

COVID-19 is extremely contagious and its rapid growth has drawn attention towards its early diagnosis. Early diagnosis of COVID-19 enables healthcare professionals and government authorities to break the chain of transition and flatten the epidemic curve. With the number of cases accelerating across the developed world, COVID-19 induced Viral Pneumonia cases is a big challenge. Overlapping of COVID-19 cases with Viral Pneumonia and other lung infections with limited dataset and long training hours is a serious problem to cater. Limited amount of data often results in over-fitting models and due to this reason, model does not predict generalized results. To fill this gap, we proposed GAN-based approach to synthesize images which later fed into the deep learning models to classify images of COVID-19, Normal, and Viral Pneumonia. Specifically, customized Wasserstein GAN is proposed to generate 19% more Chest X-ray images as compare to the real images. This expanded dataset is then used to train four proposed deep learning models: VGG-16, ResNet-50, GoogLeNet and MNAST. The result showed that expanded dataset utilized deep learning models to deliver high classification accuracies. In p

Author

Rounaq, Sumera; Shah, Shahid Munir; Aljawarneh, Mahmoud

Published

2024

Language

EN