Machine learning- based lung disease diagnosis from CT images using Gabor features in Littlewood Paley empirical wavelet transform (LPEWT) and LLE

The term 'lung disease' covers a wide range of conditions that affect the lungs, including asthma, COPD, infections like the flu, pneumonia, tuberculosis, lung cancer, COVID, and numerous other breathing issues. Respiratory failure may result from several respiratory disorders. Recently, various methods have been proposed for lung disease detection, but they are not much more efficient. The proposed model has been tested on the COVID dataset. In this work, Littlewood-Paley Empirical Wavelet Transform (LPEWT) based technique is used to decompose images into their sub-bands. Using locally linear embedding (LLE), linear discriminative analysis (LDA), and principal component analysis (PCA), robust features are identified for lung disease detection after texture-based relevant Gabor features are extracted from images. LLE's outcomes inspire the development of new techniques. The Entropy, ROC, and Student's t-value methods provide ranks for robust features. Finally, LS-SVM is fed with t-value-based ranked features for classification using Morlet wavelet, Mexican-hat wavelet, and radial basis function. This model, which incorporated tenfold cross-validation, exhibited improved classification accuracy of 95.48%, specificity of 95.37%, sensitivity of 95.43%, and an F1 score of.95. The proposed diagnosis method can be a fast disease detection tool for imaging specialists using medical images.Copyright © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model's specific decisions and, thus, creating a "black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.

An analysis of Deep Learning Models for Detection of COVID-19 Diseases

The World Health Organization has classified COVID-19 as a pandemic virus at this time. The conditions posed significant challenges for every nation on the planet, notably with the preparations made for health care and the lengthy reactions required. Because of the sudden rise in the number of infections due to COVID-19 disease and the limited resources for detecting it has become requisite to develop an artificial intelligence-based system for determining the COVID-19 disease. An increasing number of people throughout the world are testing positive for COVID-19 every day. A rapid and accurate identification of COVID-19 is a time-sensitive prerequisite for preventing and controlling the pandemic by means of appropriate isolation and medical treatment. The significance of the current work lies in its discussion of the overview of the deep learning approaches with diagnostic imaging. This includes topics such as various deep learning models and its impact in efficiently detecting the virus transmitted indications. © 2022 IEEE.

Adversarial Machine Learning Protection Using the Example of Evasion Attacks on Medical Images

This work considers evasion attacks on machine learning (ML) systems that use medical images in their analysis. Their systematization and a practical assessment of feasibility are carried out. Existing protection techniques against ML evasion attacks are presented and analyzed. The features of medical images are given and the formulation of the problem of evasion attack protection for these images based on several protective methods is provided. The authors have identified, implemented, and tested the most relevant protection methods on practical examples: an analysis of images of patients with COVID-19.

A Comparative and Extensive Study of Covid 19 Diagnosis using Lung Ultrasound Images

The COVID-19 pandemic has resulted in a worldwide health crisis that has affected all facets of human existence and has brought the world to a halt. The most important pre-requisite for COVID-19 diagnosis is early detection. Machine learning algorithms can help in speeding up the process while saving money and effort. Following a comprehensive background study on the various medical imaging options available, it was discovered that there are few surveys focusing on COVID-19 identification based on Lung Ultrasound. The feasibility of lung ultrasound is visible from the survey. In this paper, huge efforts have been undertaken to study the road-map of lung ultrasound markers for detecting COVID-19. The detection of abnormal A lines, B lines and pleural lines or traces in ultrasound images will aid in the rapid identification and control of the ongoing COVID- 19 epidemic. The numerous deep learning models will make diagnosis easier and more accurate, assisting doctors and front-line employees in this pandemic emergency. © 2022 IEEE.