Medical Diagnosis projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Medical Diagnosis projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Medical Diagnosis Projects
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A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model
This project aims to develop an intelligent deep learning system for classifying diabetic retinopathy based on fundus images. It focuses on identifying both early and severe retinal lesions that previous models often ignored. The approach combines GoogleNet and ResNet with an adaptive particle swarm optimizer to improve feature extraction. The extracted features are then tested with machine learning models such as random forest and SVM. The hybrid model achieves high accuracy and shows strong potential for improving diagnosis of DR severity levels. -
Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling – A Critical Review
The project aims to predict chronic diseases early using machine learning. It focuses on improving medical data quality by handling missing values, outliers, feature selection, normalization, and imbalance. The goal is to choose the best machine learning methods that give high accuracy and reliability. The study also reviews existing research and highlights challenges and future directions for better prediction performance. -
Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning
The project aims to develop an objective method to assess motor symptoms in Parkinson’s disease patients. It uses a Leap Motion sensor to capture hand movements during standard tasks. The movement data are processed to extract detailed features. Machine learning techniques are then applied to select the most important features and predict symptom severity. The approach improves accuracy and reduces reliance on expert evaluation. -
A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis
This project focuses on detecting brain disorders using brain activity data. It combines diagnosis and explanation in a single system to make results more reliable. The system learns which brain connections are important for each person. It can also simulate changes in brain activity to understand how disorders affect the brain.
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