Enterprise-grade skin lesion screening powered by deep learning

DermaSense helps clinicians and research teams standardize image intake, run consistent CNN inference, and document precautions and care guidance — with full audit trails per user account.

TensorFlow inference pipeline

Images are resized, normalized, and scored through your deployed Keras model with calibrated confidence metrics.

Clinical knowledge layer

Precautions and formulary hints are mapped per disease class and maintained centrally by your administrators.

Per-tenant isolation

Session-based access control ensures each user only sees their own uploads, history, and downloadable reports.

Operational workflow

From capture to archived PDF-ready report in four controlled steps — ideal for capstone demonstrations and pilot clinics.

1

Secure intake

JPEG/PNG uploads with size and integrity checks; preview before submission reduces bad captures.

2

Automated preprocessing

Images are standardized to model input tensors with consistent scaling for reproducible scores.

3

Evidence bundle

Each prediction stores disease label, softmax confidence, timestamp, and links to education content.

4

Reporting

Print-optimized HTML reports support browser PDF export for charts and EMR attachment workflows.

Designed for responsible deployment

This platform is intended for educational and decision-support contexts. Always validate model performance on your target population and follow local regulatory requirements for medical devices. Administrators can rotate model weights, remap class order, and purge obsolete records without touching unrelated tenants.

Dataset alignment

Train with curated public corpora such as the Skin Diseases image dataset on Kaggle, then upload your .keras/.h5 artifact and label map.

Provision a workspace