About the project

Why OncoSecure AI exists

Medical AI works best when clinicians can see why a model made its prediction and how patient data is handled. OncoSecure AI is an educational exploration of both — combining multi-cancer classification with explainable AI and privacy-first design.

Project motivation

Clinical relevance

Cancer is among the leading causes of mortality globally, and imaging is a core part of screening and diagnosis. AI can support — but must never replace — expert review.

Trust through transparency

Black-box predictions are hard to act on. OncoSecure pairs every result with a visual explanation so users can assess whether the model is attending to the right regions.

Privacy as a default

Medical images carry sensitive information. The platform is designed from the ground up to validate, process, and discard uploads without persistent storage or third-party exposure.

Why these three cancer categories

Brain tumor, breast cancer, and lung cancer were selected because they span diverse imaging modalities (MRI, histopathology/ultrasound, CT), have well-established public datasets, and represent clinically significant disease areas where AI-assisted triage has been actively researched.

Brain Tumor

MRI

Covers 4-class classification (Glioma, Meningioma, Pituitary, No Tumor) using structural MRI patterns. Widely used in explainable AI benchmarks.

Breast Cancer

Histopathology / Ultrasound

Binary benign vs. malignant classification. Illustrates how small architectural changes can affect class probability distributions.

Lung Cancer

Three-class CT-based screening (Normal, Benign, Malignant). Demonstrates attention-region shifts between healthy and pathological scans.

The role of AI

Pattern recognition at scale, not clinical judgment

Deep learning models can learn visual patterns from thousands of annotated scans — patterns that humans may struggle to articulate. In OncoSecure AI, the model acts as an assistive layer: it surfaces a predicted class and probability distribution, giving the user a starting point for further review. It never finalizes a diagnosis.

The role of explainable AI

Every prediction should come with a justification

Grad-CAM (Gradient-weighted Class Activation Mapping) highlights the regions of the input that most influenced the model’s prediction. A plausible heatmap does not guarantee a correct prediction, but a clearly implausible one (e.g., attention on image borders or annotation text) is a strong red flag. This platform treats explainability as a first-class feature, not a post-hoc addition.

The role of security & privacy

Handling medical images with appropriate care

Uploads are validated by MIME type and size limits on both the client and the server. Images are processed transiently during inference and are not persisted to disk in this educational build. There is no patient identifier handling, no EMR linkage, and no third-party upload. When wiring a real inference backend, similar principles should be enforced end-to-end.

Educational use only — not a medical device

OncoSecure AI is an academic decision-support demonstration intended for coursework, portfolio evaluation, and internship presentation. It is not a registered medical device, has not undergone clinical validation, and must not be used to diagnose, triage, or treat any patient. All clinical decisions should be made by qualified healthcare professionals based on validated tools and complete clinical context.

Try the analyze flow

Upload a sample scan and see the full prediction + explainability pipeline in action.

Go to analyze