2024 IEEE Biomedical project list on embedded based for M.Tech / MS / BE / B.Tech / Diploma / M.Sc students in Trichy

Final Year Project Ideas and list of Biomedical Engineering projects

    When designing for human health, biomedical engineers use their expertise and knowledge of contemporary biological concepts, which sets them apart from other engineering specialties. In order to improve human health, biomedical engineering combines human biology with other engineering disciplines such as mechanical engineering, electrical engineering, chemical engineering, materials science, chemistry, mathematics, and computer science and engineering.

    The fusion of the engineering and medical fields for healthcare and medical applications is known as biomedical engineering. In order to better handle healthcare challenges, Galwin Tech Provides biomedical engineering combines biological support with electronics, mechanical, and other engineering principles. Blood pressure, temperature, and other metres and readers, ventilators, prosthetics, and other devices are a few examples of biomedical engineering.

    Final Year Project Ideas and list of Biomedical Engineering projects.

    • Visualizing Wellness ML-Powered Heart Disease Prediction with GUI
    • Improving Maternal Healthcare: Machine Learning Algorithms for Preeclampsia Prediction
    • Advanced Analytics in Vascular Health: Machine Learning Applications for Varicose Veins
    • Personalized Treatment Strategies for Leukemia Patients: An ML Perspective
    • A Comparative Analysis of Classification Techniques in Diabetic Retinopathy Detection
    • Beyond Mammography: Enhancing Breast Cancer Detection through Classification Models
    • Machine Learning in Ophthalmology: Classifying Eye Tumors for Early Detection
    • Predicting Asthma Exacerbations: A Machine Learning Revolution in COPD Healthcare
    • Revolutionizing Eye Care: ODIR System for Automated Disease Recognition
    • Stroke Risk Prediction with Machine Learning Techniques
    • Innovations in Healthcare: Machine Learning for Early Lung Cancer Detection
    • Health Informatics: A Comprehensive Study on Disease Prediction Using ML and Symptom Analysis
    • Image Analysis and Classification for Sinus Diagnosis with ML
    • Towards Precision Medicine: Machine Learning for Pneumonia Prognostication
    • Enhancing Bone Injury Diagnosis: A Machine Learning Approach for Crack Detection
    • Enhanced Brain Tumor Diagnosis through Machine Learning on Medical Imaging
    • Diagnostic Precision: Machine Learning Applications in Identifying Kidney Diseases
    • CancerWise: Wisdom in Oral Cancer Detection through Machine Learning
    • MediMind Chatbot: Smart Health Conversations at Your Fingertips
    • Detection of skin sepsis identification with convolutional neural networks
    • Revolutionizing Input Methods Hand Gesture Recognition System
    • Blind assistant system- object detection for visually impaired people using deep learning
    • Identifying Medicine Names with Smart Technology
    • Synchronized Vision Analysis with Advanced Eye Tracking
    • Humanizing Assistive Technology: Tetraplegia Support via Facial Movements
    • Image Processing Techniques for Accurate Diabetic Retinopathy Diagnosis
    • YOLO Visionary Empowering the Blind through Object Recognition
    • Advancing Ocular Health Eye Tumor Detection through Technology
    • Automated Medication Dispensing System With Facial Recognition
    • Smart Vision Assistant For The Visually Impaired: Object Detection And Auditory Feedback

    IEEE projects for biomedical engineering

    • Microcontroller based anesthesia machine
    • A Bendable and Wearable Cardio respiratory Monitoring Device Fusing Two Noncontact Sensor Principles (IEEE 2024).
    • The patient-centric mobile healthcare system enhancing sensor connectivity and data interoperability (IEEE 2024).
    • An Energy-Efficient Adaptive Sensing Framework for Gait Monitoring Using Smart Insole .
    • Controlling applications with hand gestures using sixth sense prototype / wear your world using sixth sense technology
    • Eye ball Sensor for automatic Wheel Chair for paralyzed patients
    • A sleep apnea keeper in a wearable device for Continuous detection and screening during daily life
    • Controlling applications with hand gestures using sixth sense prototype / wear your world using sixth sense technology
    • Voice Actuated Speaker-Dependent Control System for Hospital Bed
    • Portable ECG Monitoring Device with Bluetooth and Holter Capabilities for Telemedicine Applications
    • Designing of Tele Medicine application on ARMTDMI architecture
    • Eyeball Sensor for automatic Wheel Chair for paralyzed patients
    • Fully Secured & Automated Corporate Environment Using Biometric Device
    • An Adaptable and Extensible Mobile Sensing Framework for Patient Monitoring
    • Mobile Data Acquisition towards Contextual Risk Assessment for Better Disease Management in Diabetes
    • BIO-monitoring System with Conductive Textile Electrodes Integrated into T-shirt
    • Child Activity Recognition Based on Cooperative Fusion Model of a Triaxial Accelerometer and a Barometric Pressure Sensor
    • Using of Raspberry Pi for Data Acquisition from Biochemical Analyzers
    • Design and Implementation 0/ Real Time Embedded Tele-Health Monitoring System
    • A sleep apnea keeper in a wearable device for Continuous detection and screening during daily life Microcontroller based anaesthesia machine



As a result of diabetes mellitus, which causes lesions on the retina of the eyes, diabetic retinopathy is a degenerative condition that affects the eyes. For diabetic patients, especially the working-age population in poor countries, diabetic retinopathy is thought to be the main cause of blindness. Since the condition is irreversible, treatment focuses on maintaining the patient's level of eyesight. Regular screening is crucial for diabetic patients to ensure that DR is detected at an early stage. DR detection traditionally involves a physician’s examination of retinal imaging for the shape and appearance of different types of lesions. Traditional screening of retinal diseases requires multiple stages of scans followed by filtration techniques to narrow down the subject samples. Optical coherence tomography (OCT) and spatial domain optical coherence tomography (SD-OCT) are examples of scans performed during the screening stage Machine learning-based medical image analysis has proven competency in assessing retinal fundus images, and the utilization of deep learning algorithms has aided the early diagnosis of Diabetic Retinopathy. The paper discusses the available retinal fundus datasets for Diabetic Retinopathy that are used for tasks such as detection and classification. The proposed system is a Deep Learning-based system for classifying DR aneurysms (MA), haemorrhages (HM), and exudates, using Convolutional Neural Network (CNN) algorithm. The system consists of three stages: image pre-processing, feature extraction, and classification. In the first stage, the retinal fundus images are pre-processed to enhance their quality and remove noise. In the second stage, features are extracted from the pre-processed images using CNN algorithm. The system achieves high accuracy in classifying DR aneurysms (MA), haemorrhages (HM), and exudates, and can potentially be used for large-scale screening programs to improve the early detection and treatment of DR. In addition to DR, this system consists of a gas sensor that detects the level of acetone in the patient's breath. .

2. Digital Breast Tomosynthesis Deep Learning Advancements In Cancer Detection .

Breast cancer is a prevalent and potentially life-threatening disease, necessitating early detection for effective treatment. In this context, leveraging advanced technologies such as machine learning offers promising avenues for improving diagnostic accuracy. This project focuses on the development and implementation of a Convolutional Neural Network (CNN) algorithm to enhance the detection of breast cancer cells from histopathological images. The proposed CNN model is designed to automatically learn hierarchical features from microscopic images, enabling it to discern subtle patterns indicative of cancerous cells. The dataset used for training and evaluation encompasses a diverse range of breast tissue samples, ensuring the model's robustness across various pathological presentations. The project aims to contribute to the existing body of knowledge by providing a reliable and efficient tool for aiding pathologists in the early detection of breast cancer. The performance of the CNN algorithm is assessed through rigorous experimentation and comparison with existing methods, demonstrating its potential to significantly improve the accuracy and speed of breast cancer diagnosis. This research not only addresses a critical healthcare challenge but also underscores the transformative impact of machine learning in advancing medical diagnostics.


Pancreatic cancer is one of the most lethal forms of cancer, with a high mortality rate primarily due to late-stage diagnosis. Early detection plays a crucial role in improving patient outcomes by enabling timely intervention and treatment. In this study, we propose a novel approach for the detection of pancreatic cancer using Convolutional Neural Networks (CNNs). Leveraging the capabilities of deep learning, our CNN-based model analyzes medical imaging data, specifically computed tomography (CT) scans, to accurately identify signs of pancreatic malignancy.The proposed CNN architecture is trained on a large dataset of annotated CT scans obtained from patients with confirmed pancreatic cancer and healthy controls. Through extensive training and validation, the model learns to differentiate between normal pancreatic tissue and cancerous lesions, capturing subtle structural and textural features indicative of malignancy. Additionally, the CNN is optimized to minimize false positives and negatives, ensuring high sensitivity and specificity in cancer detection.To evaluate the performance of our approach, we conduct rigorous testing using independent datasets and benchmark against existing diagnostic methods. Our results demonstrate the efficacy of the CNN-based model in detecting pancreatic cancer with high accuracy and reliability. Moreover, we explore the potential of integrating our automated detection system into clinical workflows to aid radiologists and oncologists in early cancer diagnosis.Overall, our study presents a promising advancement in the field of pancreatic cancer detection, offering a non-invasive and efficient approach for early diagnosis. By harnessing the power of deep learning and medical imaging, our CNN-based method has the potential to revolutionize pancreatic cancer screening and improve patient outcomes through timely intervention and treatment.


The Intelligent Eyeball Controlled Wheelchair for Paralyzed Patients using Arduino is a novel assistive technology designed to improve the mobility of people who are paralyzed or have limited motor function. The system is based on a combination of an Arduino microcontroller and a camera that tracks eye movements. By monitoring the movement of the user's eyes, the system is able to control the movement of the wheelchair in real time. The system consists of a camera mounted on a pair of glasses worn by the user. The camera captures images of the user's eyes, which are then processed by an Arduino microcontroller. The microcontroller processes the eye movement data and uses it to control the movement of the wheelchair. The wheelchair is equipped with motors and wheels, which are controlled by the microcontroller. The microcontroller is programmed to interpret the user's eye movements and translate them into commands for the wheelchair's motors. The system can be programmed to recognize a range of eye movements, such as up, down, left, right, and blink, which can be used to control the wheelchair's movement. The Intelligent Eyeball Controlled Wheelchair for Paralyzed Patients using Arduino has the potential to significantly improve the mobility and quality of life of people who are paralyzed or have limited motor function. The system is affordable, easy to use, and can be customized to meet the needs of individual users. It has the potential to revolutionize the field of assistive technology and provide a new level of independence to people with disabilities.

5. Intelligent System For Alzheimer’s Risk Assessment.

The aim of this project is to develop a deep learning model based on Convolutional Neural Networks (CNNs) to accurately diagnose Alzheimer's disease (AD) using Magnetic Resonance Imaging (MRI) scans. AD is a progressive and irreversible brain disorder that affects memory and cognitive function, and its diagnosis is challenging due to the complexity and subtlety of its symptoms. In this project, we will start by collecting a large dataset of MRI scans from patients with AD, as well as healthy individuals, to train and validate the CNN model. The data will be pre-processed and augmented to improve the performance of the model. Then, we will design a CNN architecture that is capable of learning discriminative features from the MRI scans and classifying them into AD or non-AD. We will evaluate the performance of the model using various metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). We will also compare our model's performance with other state-of-the-art methods for AD diagnosis.