Hossain, Md Golam SorwarKabir, Md. SaifJalal, JunaidFahi, Fariyan ShahAhmed, Muntasir Abdullah BinDatta, AbhishekHusam Uz-Zaman, S.M2026-04-192026-04-1920262026-01ID 21221013ID 21221005ID 21321072ID 21221023http://hdl.handle.net/10361/27950Cataloged from PDF version of final year design project.Includes bibliographical references (pages 105-106).This final year design project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2026.Cardiovascular diseases remain a leading cause of mortality in Bangladesh, exacerbated by a critical shortage of pediatric cardiac specialists in rural regions. This project presents the design and implementation of a low-cost, Cloud-Connected AI Stethoscope aimed at democratizing cardiac screening. The system integrates a dual-microphone setup with Active Noise Cancellation (LMS algorithm) to capture high-fidelity heart sounds, achieving a Signal-to-Noise Ratio (SNR) improvement of +12.6 dB even in noisy clinical environments. Captured audio is digitized by an ESP32 microcontroller and transmitted via Wi-Fi to a cloud server, where a Fusion Convolutional Neural Network (CNN) detects and classifies heart murmurs with 91% accuracy. By offloading computation to the cloud, the device maintains a low unit cost of approximately 5,650 BDT while ensuring diagnostic reliability. This solution offers a scalable, affordable tool for frontline health workers to identify cardiac risks early, potentially reducing preventable deaths in underserved communities.170 pagesenBRAC University project reports 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.Digital stethoscopeCloud computingActive noise cancellationNeural networkTelemedicineMachine learning.Cloud computing.Telecommunication in medicine.Project report