A cascaded computer vision pipeline for detecting, classifying, and segmenting GI cancer lesions.
YOLOv8 localises suspicious GI regions in real time, ResNet50 classifies malignancy with high accuracy, and U-Net delineates tumour boundaries pixel by pixel — with all three stages cascaded into a single diagnostic report.
A single model asked to find, classify and outline a lesion at once does none of the three well. Endoscopy generates far more footage than a specialist can review frame by frame.
Split the problem into a cascade where each stage does one job — detect, classify, segment — and feed the outputs into one consolidated report.
YOLOv8 Lesion Detection
Real-time localization of suspicious GI regions.
ResNet50 Classification
High-accuracy malignancy classification.
U-Net Segmentation
Pixel-level tumor boundary delineation.
Cascaded Diagnostic Output
Combines detection, classification, and segmentation into one report.
Detection
Classification
Segmentation
Application
Frame intake
Endoscopy images or frames are loaded.
Detection
YOLOv8 localises suspicious regions.
Classification
ResNet50 grades malignancy for each region.
Segmentation
U-Net outlines the tumour boundary.
Reporting
All three outputs are cascaded into one report.
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Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.
That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.
We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.
Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.
Get complete source code, documentation, PPT, explanation, and support.
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