0000000000522831
AUTHOR
Micheal Dutt
A Multi-layer Feed Forward Neural Network Approach for Diagnosing Diabetes
Diabetes is one of the worlds major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in an increase in serious complications such as heart attacks and deaths. Early diagnosis of diabetes, particularly of type 2 diabetes, is critical since it is vital for patients to get insulin treatments. However, diagnoses could be difficult especially in areas with few medical doctors. It is, therefore, a need for practical methods for the public for early detection and prevention with minimal intervention from medical professionals. A promising method for automated diagnosis is the use of artificial…
SleepXAI: An explainable deep learning approach for multi-class sleep stage identification
AbstractExtensive research has been conducted on the automatic classification of sleep stages utilizing deep neural networks and other neurophysiological markers. However, for sleep specialists to employ models as an assistive solution, it is necessary to comprehend how the models arrive at a particular outcome, necessitating the explainability of these models. This work proposes an explainable unified CNN-CRF approach (SleepXAI) for multi-class sleep stage classification designed explicitly for univariate time-series signals using modified gradient-weighted class activation mapping (Grad-CAM). The proposed approach significantly increases the overall accuracy of sleep stage classification …
Classification of Diabetes and Cardiac Arrhythmia using Deep Learning
Master's thesis Information- and communication technology IKT591 - University of Agder 2018 Deep Learning (DL) is a research area that has ourished signi cantly in the recent years and has shown remarkable potential for arti cial intelligence in the eld of medical applications. The reasons for success are the ability of DL algorithms to model high-level abstractions in the data by using automatic feature extraction property as well as signi cant amount of medical data that is available for training these algorithms. DL algorithms can learn features from a large volume of healthcare data, and then use the procured insights to assist clinical practice. We have implement DL algorithm for the c…