Zaman, ShakilaHossain, Dr. Muhammad IqbalShafin, Md. Mehtabul IslamAkhter, SabrinHasan, Mohammad ShafkatNasimuzzaman, Md.Prithul, Tamzeedur Rahman2023-08-062023-08-0620232023-01ID: 19101088ID: 18301098ID: 19101077ID: 19101051ID: 18301289http://hdl.handle.net/10361/19294Cataloged from PDF version of thesis.Includes bibliographical references (pages 42-45).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.This research aims to provide an approach for analyzing the security of the e-health care system through the use of federated learning and the pre-processing of distinct deep learning models. The infrastructure for e-healthcare services is being gradually deployed by the health sector. This method increased the safety of patients and doctors through a protected platform. As a result, it is going to replace the current health service. Even if this technology is becoming more and more widespread, a number of data security threats need to be tackled. In this research, a CNN and MLP architecture with a classification-focused approach using a number of pre trained feature extractors such as ResNet-50, VGG16, and Inception- v3 have been implemented. Additionally, various machine learning classification algorithms (such as Random Forest, and Logistic Regression) have been used to classify the images in order to compare how well they perform to a neural network approach. Federated learning has also been incorporated to increase healthcare data security as it does not transmit actual data but models. The objective is to develop a hybrid federated learning model to analyze the security of e-health data. The core premise is to utilize a methodology like federated learning, which enables a technique for creating machine learning models while safeguarding user privacy and can maintain e-health data security without transferring real-world data.45 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Federated learningMachine learninge-Health careCNNMLPRandom forestLogistic regressionMedical informatics.Medical telematics.Thesis