Detection of COVID 19 using Transfer Learning and Progressive Resizing on HR-CT Images

Coronavirus disease-19 spread has grown to almost every corner of the globe. As a result, it is necessary to take steps toward a much earlier diagnosis of its infection. Chest X-Rays, Computed Tomography, and RT-PCR are early-stage diagnostic techniques. Visually detecting and inspecting these clinical images for any hidden anomalies is a time-consuming task. Transfer Learning in medical imaging has a lot of research potential. The method proposed here is a transfer learning-based binary classification model which predicts whether a Lung CT image has SARS-CoV-2 infection. It has a three-stage procedure for fine-tuning various pre-trained architectures. It uses progressive resizing an optimization technique in which our approach is to resize the input images to 128×128 ×3, 150×150 ×3, and 224×224 ×3 pixels and fine-tuning the neural network at each stage. As a result, CT transfer learning with progressive resizing outperforms various published models in the recent research work with improved accuracy of 97.4% with only 22 epochs. This technique may help to diagnose COVID-19 patients at an early stage and reduce the pressure on medical systems.