Biomedical Imaging projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Biomedical Imaging 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 Biomedical Imaging 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. -
PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep LearningBased Centroblast Cell Detection
This project created a computer program called PseudoCell that can find specific cells called centroblasts in medical tissue images. It helps doctors by pointing out important areas without needing them to label every cell manually. The program saves time and makes the diagnostic process faster and easier. It can remove most irrelevant parts of the images while keeping the important ones. -
Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations
This project focuses on improving eye scans called OCTA, which show the blood vessels in the retina. The researchers created a way to make realistic fake images of these vessels to help train computers. Their method helps the computer find even the smallest blood vessels more accurately. They also shared all their code and data so others can use it. -
The Latent Doctor Model for Modeling Inter-Observer Variability
This project focuses on improving medical image analysis. It teaches a computer model to understand not just the most common expert opinion, but also the differences between experts. The model can predict both the likely correct label and how uncertain experts might be. It works better than traditional methods in grading prostate tumors and other tasks. -
Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans
This project developed an artificial intelligence system to detect and measure lung cavity lesions from CT scans. The system can automatically find and outline the affected areas. It also calculates important features like size and thickness. This tool can help doctors diagnose, monitor, and track treatment of lung lesions more quickly and accurately.
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