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A crucial step in the battle against the coronavirus disease 2019 (Covid-19) pandemic is efficient screening of the Covid affected patients. Deep learning models are used to improve the manual judgements made by healthcare professionals in classifying Chest X-Ray (CXR) images into Covid pneumonia, other viral/bacterial pneumonia, and normal images. This work uses two open source CXR image dataset having a total of 15,153 (dataset 1), and 4575 (dataset 2) images respectively. We trained three neural network models with a balanced subset of dataset 1 (1345 images per class), balanced dataset 2 (1525 images per class), and an unbalanced full dataset 1. The models used are VGG16 and Inception Resnet (IR) using transfer learning and a tailor made Convolutional Neural Network (CNN). The first model, VGG16 gives an accuracy, sensitivity, specificity, and F1 score of 96%, 97.8%, 95.92%, 97% respectively. The second model, IR gives an accuracy, sensitivity, specificity and F1 score of 97%, 98.51%, 97.28%, 99% respectively. The third and best proposed model, CNN gives an accuracy, sensitivity, specificity, and F1 score of 97%, 98.21%, 96.62%, 98% respectively. These performance metrics were obtained for the balanced dataset 1 and all models used 80:10:10 cross validation technique. The highest accuracy using CNN for all the three datasets are 97%, 96%, and 93% respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) is used to ensure that the model uses genuine pathology markers to generalize. [ABSTRACT FROM AUTHOR]

A review on the recent segmentation and tumor grade classification techniques of brain Magnetic Resonance (MR) Images is the objective of this paper. The requisite for early detection of a brain tumor and its grade is the motivation for this study. In Magnetic Resonance Imaging (MRI), the tumor might appear clear but physicians need quantification of the tumor area for further treatment. This is where the digital image processing methodologies along with machine learning aid further diagnosis, treatment, prior and post-surgical procedures, synergizing between the radiologist and computer. These hybrid techniques provide a second opinion and assistance to radiologists in understanding medical images hence improving diagnostic accuracy. This article aims to retrospect the current trends in segmentation and classification relevant to tumor infected human brain MR images with a target on gliomas which include astrocytoma. The methodologies used for extraction and grading of tumors which can be integrated into the standard clinical imaging protocols are elucidated. Lastly, a crucial assessment of the state of the art, future developments and trends are dissertated. [ABSTRACT FROM AUTHOR]