Biomedical Imaging Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Biomedical 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.

Biomedical Imaging Final Year Projects

A Nested Attention Guided UNet Architecture for White Matter Hyperintensity Segmentation

This project focuses on improving the detection of White Matter Hyperintensity (WMH) in brain MRI scans, which is important for predicting recovery in stroke patients. The researchers developed a new deep learning method called NAUNet++ that uses attention mechanisms and atlas images to better identify WMH regions. Their approach produces more accurate and faster segmentation results than existing methods, helping doctors assess patient prognosis more reliably.
AMSeg A Novel Adversarial Architecture Based Multi-Scale Fusion Framework for Thyroid Nodule Segmentation

This project focuses on automatically detecting and outlining thyroid nodules in ultrasound images. The researchers developed a new deep learning method that can identify nodule boundaries even when the tissue is blurry or uneven. Their system, called AMSeg, performs better than existing methods and can replace manual segmentation. This could make thyroid disease diagnosis faster and more accurate in clinical settings.
An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy

This project develops an AI-based system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to analyze images and identify diseases more accurately than traditional methods. The system improves image data with techniques like rotation and flipping and combines multiple models to make reliable predictions. Experiments show it achieves around 97% accuracy, helping doctors make faster and better treatment decisions.
Biomedical Image Analysis for Colon and Lung Cancer Detection Using Tuna Swarm Algorithm With Deep Learning Model

This project focuses on creating an AI system to detect colon and lung cancer from biomedical images. The system uses advanced image processing and deep learning techniques to analyze patient scans quickly and accurately. It combines smart algorithms to improve feature extraction and classification of cancer cells. The results show it can detect cancer with very high accuracy, reaching over 99%.
Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images

This project focuses on detecting early signs of diabetic eye disease from retinal images. It creates a method to generate and highlight disease spots using a small set of open-source images. A custom neural network is developed to classify these spots accurately. The system performs very well, achieving perfect results on the test data.
Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-Ray Images

This project focuses on using artificial intelligence to analyze chest X-rays for COVID-19 detection. The researchers developed a new method that can accurately identify infected areas, even when their size or location varies. The approach improves how the system understands both the position and details of the infection in the X-ray images. Tests on public datasets show it works better and more reliably than existing methods.
Assessing Inter-Annotator Agreement for Medical Image Segmentation

This study looks at how differences between medical experts can affect the training of AI systems for analyzing medical images. It measures how consistently multiple experts label the same lesions or abnormalities. The researchers use visual maps, statistical coefficients, and an algorithm called STAPLE to check agreement and create accurate ground truth for AI training. They tested their methods on cervical and chest X-ray images to show how combining different measures improves reliability.
Classification and Localization of Multi-Type Abnormalities on Chest X-Rays Images

This project uses deep learning to analyze chest X-ray images and detect lung problems, including COVID-19. It develops models that can not only classify different diseases but also show where they are in the lungs. By combining several detection models, it improves accuracy compared to single models. The system can help doctors make faster and more reliable diagnoses.
Computer Aided Diagnosis for Gastrointestinal Cancer Classification Using Hybrid Rice Optimization With Deep Learning

This project aims to detect stomach and digestive cancers early using computer analysis of medical images. It cleans the images and then uses advanced AI models to learn important patterns. The system chooses the best settings automatically to improve accuracy. This helps doctors identify cancer sooner and make better treatment decisions.
Deep CleanerA Few Shot Image Dataset Cleaner Using Supervised Contrastive Learning

This project focuses on improving the quality of medical images before they are used for AI diagnosis. It automatically removes noisy or unwanted parts of images using a learning model trained on only a few clean examples. The system learns to separate correct images from incorrect ones. After cleaning, the accuracy of disease classification improves significantly.
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.
Interpretation of Magnetic Resonance Images of Temporomandibular Joint Disorders by Using Deep Learning

This study uses deep learning to automatically detect Temporomandibular Disorder (TMD) from MRI scans. Researchers collected over 2500 images from 200 patients and tested several advanced neural network models to classify them. The performance of these models was measured using accuracy and other medical metrics. The results show that deep learning can successfully assist in diagnosing TMD.
Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

This project focuses on automatically detecting diseases in the gastrointestinal tract using images from a tiny camera capsule. The researchers developed a computer program that cleans the images, extracts important features, and classifies diseases like bleeding, ulcers, and polyps. They combined advanced deep learning techniques with optimization algorithms to improve accuracy. Tests on a medical image database showed the system can correctly identify diseases with over 98% accuracy.
A Deep Learning-Based Brain Age Prediction Model for Preterm Infants via Neonatal MRI

This project develops a deep learning model called BAPNET to predict the brain age of premature infants using MRI scans. It helps estimate brain maturity quickly and accurately, reducing reliance on doctors’ manual assessments. The model learns from a large dataset of infant brain images and highlights important brain regions involved in development. This can support doctors in understanding brain growth and planning early interventions for preterm infants.
Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging

This project focuses on automatically detecting brain bleeding from CT scans using artificial intelligence. It creates a smart system that learns important patterns in images to classify different types of bleeding. The model combines multiple AI techniques to improve accuracy and speed. This helps doctors diagnose and treat patients faster while reducing manual effort.

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Biomedical Imaging Project Synopsis & Presentation

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Biomedical Imaging Final Year Projects with Source Code