Medical Diagnostic Imaging Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Medical Diagnostic Imaging projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Medical Diagnostic Imaging Final Year Projects

A Comprehensive Joint Learning System to Detect Skin Cancer

This project builds a smart system that helps doctors detect skin diseases early. It studies images of the skin and learns patterns that indicate different types of diseases. The system combines two learning methods to improve accuracy. It achieves very high accuracy in identifying multiple skin conditions.
A Machine Learning Approach Using Statistical Models for Early Detection of Cardiac Arrest in Newborn Babies in the Cardiac Intensive Care Unit

This project focuses on detecting cardiac arrest in newborn babies early. It uses a machine learning model to analyze babies’ vital signs in the cardiac ICU. The model can predict cardiac arrest before it happens, allowing doctors to act quickly. This approach aims to reduce deaths and complications in newborns.
A Systematic Review on Federated Learning in Medical Image Analysis

This project reviews how Federated Learning (FL) is used for analyzing medical images while keeping patient data private. The authors collected and studied research articles to understand how FL models perform compared to traditional methods. They summarized the current methods, results, challenges, and suggested directions for future research. Overall, it gives a clear picture of FL’s role in privacy-preserving medical AI.
An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images

This project focuses on detecting tuberculosis (TB) from chest X-ray images using a new deep learning model called CBAMWDnet. The model combines advanced techniques to better understand important features in the images. Tests on large datasets show it is very accurate and performs better than many existing models. This approach can help doctors diagnose TB earlier and more reliably.
Automated Stroke Prediction Using Machine Learning An Explainable and Exploratory Study With a Web Application for Early Intervention

This project focuses on predicting strokes using machine learning. The researchers developed a system that can identify people at risk early, which may help save lives. They tested several models and found that more advanced ones achieved up to 91% accuracy. They also used techniques to explain how these models make decisions, making the predictions more understandable for medical professionals.
BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations

This project develops a computer system that can automatically detect breast cancer from ultrasound images. It uses artificial intelligence to tell apart dangerous tumors from harmless ones. The system also explains its decisions using medical features that doctors rely on. Tests show it improves diagnosis accuracy and helps doctors understand its reasoning.
BSANet High-Performance 3D Medical Image Segmentation

This project focuses on improving medical image analysis, especially for tasks like brain tumor and organ segmentation. It introduces BSANet, a 3D network that can better understand images by focusing on important areas and combining information at different scales. This helps the system capture more details and make more accurate predictions. The model is tested on standard medical datasets and shows strong performance.
Clinical Errors From Acronym Use in Electronic Health Record A Review of NLP-Based Disambiguation Techniques

This study looks at how medical records often contain confusing acronyms that can cause errors in patient care. It explains why electronic health records (EHRs) sometimes increase mistakes and why understanding these acronyms is important. The research also explores how artificial intelligence, especially machine learning, can help automatically clarify the meaning of these acronyms. Finally, it reviews how EHRs are used worldwide and the latest AI methods for reducing errors caused by unclear medical terms.
Data Augmentation Based on Generative Adversarial Networks for Endoscopic Image Classification

This project aims to help doctors detect digestive system diseases more easily using computer-based image analysis. The system trains several deep learning models to automatically classify diseases from endoscopy images. It also creates extra training images using generative models to improve accuracy. The final model shows strong and safe performance, reducing the workload on medical staff.
Effective Feature Engineering Technique for Heart Disease Prediction With Machine Learning

This project focuses on predicting heart failure early using patient health data and machine learning. The researchers developed a new method called Principal Component Heart Failure (PCHF) to select the most important features from the data. They tested several machine learning algorithms and found that a decision tree model performed the best, achieving very high accuracy. The study can help doctors detect heart failure sooner and improve patient care.
Enhancing Breast Cancer Classification in Histopathological Images through Federated Learning Framework

This study develops an automated system to detect breast cancer from medical images. It secures patient data by encrypting images and storing them safely using advanced machine learning techniques. The system then classifies the images using a deep learning model, which is optimized for better accuracy. Tests show the method is highly effective, achieving over 95% accuracy and reliability.
Failure to Achieve Domain Invariance With Domain Generalization Algorithms An Analysis in Medical Imaging

This project studies how well deep learning methods can handle new data that is different from what they were trained on. The researchers tested eight popular algorithms on medical images and regular photos. They found that all methods perform similarly, and none fully remove data-specific biases. The study shows that current techniques still struggle to generalize to unseen data, especially in important fields like medical imaging.
Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

This project focuses on helping doctors quickly understand medical images by identifying the type of imaging technique used, like X-rays or skin scans. The researchers combine visual patterns and texture details from images using deep learning to extract important features. They then merge these features to improve accuracy in classifying the images. Their method shows high precision and recall, making medical image analysis faster and more reliable.
HarDNet and Dual-Code Attention Mechanism Based Model for Medical Images Segmentation

This project focuses on improving the accuracy of medical image analysis. The researchers designed a model that better identifies important features in images and separates them from the background. It uses special modules to speed up processing and highlight both position and detail information. Tests on different medical datasets showed the method works very well, helping doctors diagnose diseases faster and more accurately.
Learning From Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis

This project focuses on improving computer programs that detect problems in medical images, like X-rays. Normally, these programs learn from labels given by doctors, but different doctors may disagree, which lowers accuracy. The researchers developed a method to combine labels from multiple doctors and figure out the most likely “true” label. This makes the program more reliable and accurate at spotting abnormalities in medical images.
Leveraging Regression Analysis to Predict Overlapping Symptoms of Cardiovascular Diseases

This project uses deep learning to predict heart diseases early from patient medical records. The researchers trained a model on over 2,600 records, including age, symptoms, and disease details. They used a special neural network called LSTM to improve prediction by analyzing combinations of symptoms. Their method achieved up to 91% accuracy in predicting cardiovascular problems.
Medical Image Segmentation Based on Transformer and HarDNet Structures

This project improves medical image segmentation, which helps doctors detect diseases more accurately. It uses a new network with two encoders to capture both local details and overall image features. A special fusion module combines information from different layers to boost accuracy. Tests on several medical datasets show better results in identifying disease areas, helping in early diagnosis and treatment.
Medical Ultrasound Image Segmentation With Deep Learning Models

This project focuses on improving the analysis of medical ultrasound images. The researchers created a new model called ConvTrans-Net that combines a transformer and a deep neural network. It helps accurately identify and segment lesion areas in ultrasound scans. The model showed high precision and recall, making it more effective than some existing methods.
Multi-View Computed Tomography Network for Osteoporosis Classification

This study focuses on detecting early bone loss conditions, like osteopenia and osteoporosis, from CT scans. The researchers developed a new deep learning model called MVCTNet, which uses two images from a CT scan to automatically identify these conditions. Their approach avoids manual image cropping and improves accuracy compared to previous methods. Tests on nearly 3,000 patients’ CT images show that the model performs well and could help with earlier and easier diagnosis.
Oppositional Jellyfish Search Optimizer With Deep Transfer Learning Enabled Secure Content-Based Biomedical Image Retrieval

This project focuses on securely storing and retrieving medical images like X-rays, MRIs, and CT scans. It uses advanced deep learning techniques to extract important features from the images. The system encrypts images to keep them safe while allowing accurate searching and matching. Tests show that this method works better than existing approaches.

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Medical Diagnostic Imaging Final Year Projects with Source Code