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Deep learning is gaining instant popularity in computer aided diagnosis ofCOVID-19. Due to the high sensitivity of Computed Tomography (CT) to thisdisease, CT-based COVID-19 detection with visual models is currently at theforefront of medical imaging research. Outcomes published in this direction arefrequently claiming highly accurate detection under deep transfer learning.This is leading medical technologists to believe that deep transfer learning isthe mainstream solution for the problem. However, our critical analysis of theliterature reveals an alarming performance disparity between differentpublished results. Hence, we conduct a systematic thorough investigation toanalyze the effectiveness of deep transfer learning for COVID-19 detection withCT images. Exploring 14 state-of-the-art visual models with over 200 modeltraining sessions, we conclusively establish that the published literature isfrequently overestimating transfer learning performance for the problem, evenin the prestigious scientific sources. The roots of overestimation trace backto inappropriate data curation. We also provide case studies that consider morerealistic scenarios, and establish transparent baselines for the problem. Wehope that our reproducible investigation will help in curbing hype-drivenclaims for the critical problem of COVID-19 diagnosis, and pave the way for amore transparent performance evaluation of techniques for CT-based COVID-19detection.

Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought