Covid-19 Variants Detection & Classification Using Self Proposed Two stage MNN-2: Robust Comparison with Yolo V5 & Faster R-CNN

Covid-19 is unpredictable evolutionary discipline which requires continuous advancements for its appropriate Detection & Classifications which can be helpful for bio-medical stream. In this research, two dimensions are covered that is detection & classification using self-proposed 2 stage learning detector. Detection of different variants of Covid-19 are performed using images of CT-Scan and X-Rays of effected lungs. Furthermore, classification of different variants is carried out. Dataset of 27000 indigenous images were used for detection & classification purposed. Moreover, in depth survey & comparison is carried out with state-of-the-art Yolo v5 single state detector & Faster R-CNN 2 stage detector. Accuracy analysis of self-proposed 2 stage detector was 91.66% & 87.9% for detection & classification in comparison with YOLOv5 which had accuracy of 92.8% & 87.175% for detection & classification. Moreover, in comparison with Faster R-CNN which had accuracy of 94.8% & 87% The training analysis was performed on Nvidia T4 (16GB GDDR6). Self-proposed MNN-2 superseded Yolov5 & faster R-CNN in real time video analysis with least real time rate at FPS 30 at duration of 72 min video.