A deep learning system that detects tuberculosis from chest X-ray images via a web interface.
ResNet50 and EfficientNet architectures predict tuberculosis from chest X-rays with a reliable training and model-persistence workflow. Grad-CAM highlights the infected regions on the image, and a Flask interface returns an instant prediction on upload.
Tuberculosis screening depends on radiologists who are scarce in exactly the regions where TB burden is highest, and a prediction without visual evidence gives a clinician nothing to check.
Deploy a high-accuracy classifier behind a simple upload interface and attach Grad-CAM overlays, so a health worker sees both the call and the region that produced it.
ResNet50/EfficientNet Classification
High-accuracy TB prediction from X-ray images.
Trained Pipeline & Model Persistence
Reliable training and model-saving workflow for deployment.
Grad-CAM Visualization
Highlights infected regions on the X-ray.
Flask Web Prediction
Upload an X-ray and receive an instant prediction.
Deep Learning
Explainability
Application
X-ray upload
A chest X-ray is submitted through the web interface.
Preprocessing
The image is resized and normalised for the model.
Classification
The CNN predicts TB presence with a confidence score.
Visualisation
Grad-CAM overlays the regions driving the prediction.
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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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