Gamal Farouk

Work place: Computer Science Department, Faculty of Computers and Information, Menoufia University, Shebin Elkom 32511, Egypt



Research Interests: Software Engineering, Artificial Intelligence, Distributed Computing, Image Processing, Data Mining, Database Management System


Gamal. F. Elhady received the B.Sc, M.Sc and PhD degree in Computer Science at Faculty of Science, in 1998 and 2006, Mansoura University, Egypt. During 1998 and 2006, he works as the researcher student and Lecturer Assistance in Faculty of science computer science Dept. He is member of IAENG in USA (# 108463). His research interest includes software programing, software testing, distributed system, data mining, database, Artificial intelligent, image processing and bioinformatics.

Author Articles
Heart Disease Prediction Using Modified Version of LeNet-5 Model

By Shaimaa Mahmoud Mohamed Gaber Gamal Farouk Arabi Keshk

DOI:, Pub. Date: 8 Dec. 2022

Particularly compared to other diseases, heart disease (HD) claims the lives of the greatest number of people worldwide. Many priceless lives can be saved with the help of early and effective disease identification. Medical tests, an electrocardiogram (ECG) signal, heart sounds, computed tomography (CT) images, etc. can all be used to identify HD. Of all sorts, HD signal recognition from ECG signals is crucial. The ECG samples from the participants were taken into consideration as the necessary inputs for the HD detection model in this study. Many researchers analyzed the risk factors of heart disease and used machine learning or deep learning techniques for the early detection of heart patients. In this paper, we propose a modified version of the LeNet-5 model to be used as a transfer model for cardiovascular disease patients. The modified version is compared to the standard version using four evaluation metrics: accuracy, precision, recall, and F1-score. The achieved results indicated that when the LeNet-5 model was modified by increasing the number of used filters, this increased the model's ability to handle the ECGs dataset and extract the most important features from it. The results also showed that the modified version of the LeNet-5 model based on the ECGs image dataset improved accuracy by 9.14 percentage points compared to the standard LeNet-5 model.

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