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

Abstract

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-theart 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 Self-proposed MNN-2 superseded Yolov5 & faster R-CNN in real time video analysis with least real time rate at FPS 30 at duration of 72 min video.

Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

“…With the evolution of artificial intelligence (AI) and deep learning (DL), solutions based on specially designed neural networks have become more accurate and reliable [23]. Neural networks, particularly CNNs, have been used in various applications involving disease diagnosis [24][25][26]. The use of computer vision techniques to categorize rice leaf diseases has gained popularity in recent years.…”