A deep learning diagnostic tool that detects oral cancer from oral cavity images.
A CNN classifies malignant versus benign oral lesions from images, with preprocessing normalising and enhancing input for consistency. Grad-CAM highlights the regions driving each prediction, and a web interface returns an instant diagnostic result on upload.
Oral cancer is highly treatable when caught early and frequently detected late, particularly in regions where tobacco use is high and specialist screening is scarce.
Enable screening from a standard oral photograph with visual explanation attached, so a general practitioner or health worker can triage cases that need specialist referral.
CNN-Based Classification
Detects malignant vs benign oral lesions from images.
Image Preprocessing
Normalizes and enhances images for consistent model input.
Grad-CAM Explainability
Highlights the regions driving each prediction.
Web-Based Prediction Interface
Upload an image and receive an instant diagnostic result.
Deep Learning
Explainability
Application
Image upload
An oral cavity image is submitted.
Preprocessing
The image is normalised and enhanced.
Classification
The CNN predicts malignant or benign.
Explanation
Grad-CAM overlays the deciding regions.
Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.
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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