HD-REGNET: Heart Disease Prediction Using REGNET in Image Processing

HD-REGNET: Heart Disease Prediction Using REGNET in Image Processing

Publication Date : 2024-08-17
Author(s) :

Suhasini. R, G. Jini mol

           
Article Name :

HD-REGNET: Heart Disease Prediction Using REGNET in Image Processing

Abstract :

Abnormal functionality of the heart due to any cause is known as heart disease. It primarily affects older persons. Every year, almost six out of ten persons diagnosed with heart diseases are above the age of 65. In this paper a novel HD RegNet have been proposed for heart disease prediction using CXR images. Initial the input CXR images are pre processed using Adaptive Gaussian Star Filtering. Then the pre processed images are subjected into RegNet for extracting the features. The best features are selected using Tyrannosaurus optimization Algorithm. Finally the normal and abnormal classes of heart diseases classified utilizing the Deep Neural Network (DNN). The CHD dataset are utilized to evaluate the performance of the developed work interms of performance metrics such as accuracy, recall and precision. The proposed HD RegNet attains an accuracy rate of 99.53% in the normal class and 99.37% in the abnormal class. It achieves an overall accuracy rate 0.37%, 0.52% and 0.75% compared to the existing methods such as RFRF ILM [12], LU Net [17] and CNN & Bi LSTM [18] respectively.

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