Malathy Jawahar

This article presents the design of a robust leather species identification technique. It aims to intertwine deep learning with leather image analysis. Hence, this work collects and analyzes large-scale leather image data for diverse learning. The data involve 7600 unique images with species-distinct and varied pore patterns from four species. It proposes a novel dual-stream architecture for accurate leather image classification. It is a fusion of local binary pattern-based texture analysis and MobileNet-based adaptive feature learning, hence the name LBPMobileNet. The former highlights the local structural pattern of an image, and the latter efficiently learns the species’ uniqueness. The dual-stream model analyzes two sources of images to provide more reliable and robust learning from different textured images. At the same time, it adopts two MobileNets to design a computationally efficient model. Thus, the proposed model utilizes limited resources and provides 96.45% accurate leather image classification. Further, the performance analysis affirms the generalization ability of the proposed model by predicting species from leather images with ideal and complex behavior. It also validates the robustness and computational efficiency of the proposed model with the state-of-the-art deep learning models. Thus, this study proves the relevance of local binary patterns, fused feature analysis, dual-stream architecture, and deep learning for efficient leather image analysis. It, thereby, assists the leather experts by developing an automatic and accurate species prediction method.

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.