A deep learning diagnostic tool that detects lung cancer from chest CT scan images.
A CNN detects malignant nodules from CT scan slices, with preprocessing and segmentation isolating lung regions so analysis stays focused. Grad-CAM visualises the regions influencing each prediction, and a web interface returns an instant diagnostic result on upload.
Lung cancer has one of the worst survival rates largely because it is found late. A CT study contains hundreds of slices, and small early-stage nodules are easy to miss under review fatigue.
Segment the lung region first so the classifier is not distracted by surrounding anatomy, then flag candidate nodules slice by slice with visual evidence for the radiologist to confirm.
CNN-Based Classification
Detects malignant nodules from CT scan slices.
Image Preprocessing & Segmentation
Isolates lung regions for focused analysis.
Grad-CAM Explainability
Visualizes regions influencing each prediction.
Web-Based Prediction Interface
Upload a scan and receive an instant diagnostic result.
Deep Learning
Medical imaging
Application
Scan upload
CT scan slices are submitted for analysis.
Lung segmentation
Lung regions are isolated from surrounding anatomy.
Nodule detection
The CNN classifies malignant nodules.
Explanation
Grad-CAM visualises the influencing regions.
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Get complete source code, documentation, PPT, explanation, and support.
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