Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions by analyzing the Pap smear image. The majority of existing work cover binary classification approaches using various classifiers and Convolution Neural Networks. However, they suffer from inherent challenges for minute feature extraction and precise classification. We propose a novel methodology to carry out the multiclass classification of cervical cells from Whole Slide Images (WSI) with optimum feature extraction. The actualization of Conv Net with Transfer Learning technique substantiates meaningful Metamorphic Diagnosis of neoplastic and pre-neoplastic lesions. As the Progressive Resizing technique (an advanced method for training ConvNet) incorporates prior knowledge of the feature hierarchy and can reuse old computations while learning new ones, the model can carry forward the extracted morphological cell features to subsequent Neural Network layers iteratively for elusive learning. The Progressive Resizing technique superimposition in consultation with the Transfer Learning technique while training the Conv Net models has shown a substantial performance increase. The proposed binary and multiclass classification methodology succored in achieving benchmark scores on the Herlev Dataset. We achieved singular multiclass classification scores for WSI images of the SIPaKMed dataset, that is, accuracy (99.70%), precision (99.70%), recall (99.72%), F-Beta (99.63%), and Kappa scores (99.31%), which supersede the scores obtained through principal methodologies. GradCam based feature interpretation extends enhanced assimilation of the generated results, highlighting the pre-malignant and malignant lesions by visual localization in the images.

A viral outbreak with a lower respiratory tract febrile illness causes pulmonary syndrome named COVID-19. Pulmonary consolidations developed in the lungs of the patients are imperative factors during prognosis and diagnosis. Existing Deep Learning techniques demonstrate promising results in analyzing X-ray images when employed with Transfer Learning. However, Transfer Learning has its inherent limitations, which can be prevaricated by employing the Progressive Resizing technique. The Progressive Resizing technique reuses old computations while learning new ones in Convolution Neural Networks (CNN), enabling it to incorporate prior knowledge of the feature hierarchy. The proposed classification model can classify pulmonary consolidation into normal, pneumonia, and SARS-CoV-2 classes by analyzing X-rays images. The method exhibits substantial enhancement in classification results when the Transfer Learning technique is applied in consultation with the Progressive Resizing technique on EfficientNet CNN. The customized VGG-19 model attained benchmark scores in all evaluation criteria over the baseline VGG-19 model. GradCam based feature interpretation, coupled with X-ray visual analysis, facilitates improved assimilation of the scores. The model highlights its strength to assist medical experts in the COVID-19 identification during the prognosis and subsequently for diagnosis. Clinical implications exist in peripheral and remotely located health centers with the paucity of trained human resources to interpret radiological investigations' findings.

Spurt upsurge in violent protest and armed conflict in populous, civil areas has upstretched momentous concern worldwide. The unrelenting strategy of the law enforcement agencies focuses on thwarting the conspicuous impact of violent events. Increased surveillance using a widespread visual network supports the state actors in maintaining vigilance. Minute, simultaneous monitoring of numerous surveillance feeds is a workforce-intensive, idiosyncratic, and otiose method. Significant advancements in Machine Learning (ML) show potential in realizing precise models to detect suspicious activities in the mob. Existing pose estimation techniques have privations in detecting weapon operation activity. The paper proposes a comprehensive, customized human activity recognition approach using human body skeleton graphs. The VGG-19 backbone extracted 6600 body coordinates from the customized dataset. The methodology categorizes human activities into eight classes experienced during violent clashes. It facilitates alarm triggers in a specific activity, i.e., stone pelting or weapon handling while walking, standing, and kneeling is considered a regular activity. The end-to-end pipeline presents a robust model for multiple human tracking, mapping a skeleton graph for each person in consecutive surveillance video frames with the improved categorization of suspicious human activities, realizing effective crowd management. LSTM-RNN Network, trained on a customized dataset superimposed with Kalman filter, attained 89.09% accuracy for real-time pose identification.