Computer-aided Diagnosis Projects for ME, MTech, Masters, MS abroad, and PhD students. These Computer-aided Diagnosis ieee projects are implemented with future work and extension for final year students with research paper writing and publishing. These research projects guide final year 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 Computer-aided Diagnosis Projects
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LYSTO The Lymphocyte Assessment Hackathon and Benchmark Dataset
This project, called LYSTO, organized a fast-paced competition to count immune cells called lymphocytes in cancer tissue images. Participants had only a few hours to develop methods to analyze colon, breast, and prostate cancer samples. The results showed some methods matched expert pathologists in accuracy. The dataset and evaluation tools are now available online for further research. -
The Use of Machine Learning in Eye Tracking Studies in Medical Imaging A Review
This project reviews how machine learning can be applied to eye tracking in medical imaging. It looks at the equipment, software, and methods used in existing studies. The goal is to see how tracking doctors’ eye movements can help improve diagnosis, detect errors, and reduce fatigue. The study also gives suggestions for future research in this area. -
Coronary Artery Disease Classification With Different Lesion Degree Ranges Based on Deep Learning
This project studies how artificial intelligence can help doctors analyze heart artery images. The researchers used deep learning to automatically classify parts of these images as having artery blockages or not. They tested several AI models and measured how accurate they were depending on how severe the blockages were. The results show that AI can be very accurate, but smaller blockages are harder to detect.
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