AI Breast Cancer Mammogram Scan
EfficientNet-B4 · FPN · CBAM

AI-Assisted
Breast Cancer
Detection

DeepMammo is a clinical-grade multi-task AI system built for breast cancer triage. It instantly analyzes mammograms to detect abnormalities, classify benign/malignant pathology, segment lesions, and compile a structured clinical report — in under 3 seconds.

Run Analysis View Architecture
45k+ Training Images
99% Early-Stage Survival
<3s Report Generation
DeepMammo Analysis Live
Abnormality
Mass
Pathology
Benign
Coverage
4.7%
Triage
Moderate
AI Confidence 91%
0%
Five-year survival rate when breast cancer is detected at the localised stage.
Early Detection Outcome
1 in 0
Women will develop breast cancer in their lifetime — making routine screening critical.
Global Incidence
0k+
Augmented mammogram images used to train and validate the DeepMammo model pipeline.
Training Dataset
The Pipeline

How DeepMammo Works

From raw mammogram upload to a structured clinical PDF report in four precise steps.

1
Upload Scan

Submit a PNG or JPEG mammogram image via the API or Developer Playground. Supports images up to 20 MB.

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2
Preprocess

CLAHE contrast enhancement, Otsu thresholding, and breast tissue isolation prepare the image for inference.

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3
Deep Inference

EfficientNet-B4 encoder with FPN neck and CBAM attention simultaneously runs segmentation and dual classification.

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4
Generate Report

A structured clinical report with findings, impression, therapy regimen, and patient handout is generated in seconds.

Model Architecture

Four Tasks. One Inference Pass.

DeepMammo solves detection, classification, segmentation, and reporting simultaneously using a shared EfficientNet-B4 backbone.

96.2% AUC
Abnormality Detection

Identifies and classifies mammographic abnormalities as mass or calcification with high sensitivity. Multi-head classification output with calibrated probability scores.

94.7% Accuracy
Pathology Classification

Distinguishes benign from malignant findings with calibrated confidence scores. Benign/malignant probability outputs guide clinical urgency triage decisions.

0.78 Dice Score
Lesion Segmentation

FPN decoder produces pixel-wise segmentation masks highlighting the abnormal region. Quantifies coverage percentage for area-based clinical interpretation.

Groq Llama-3.3-70B
Clinical Report Generation

Groq-powered LLM generates a structured clinical narrative: findings, impression, therapy regimen, patient handout, and a downloadable PDF report.

94.7%
Pathology Accuracy
Model Performance

Built for Clinical Precision

DeepMammo's EfficientNet-B4 backbone with Feature Pyramid Network neck achieves 94.7% pathology accuracy and 96.2% abnormality AUC on a held-out validation set of augmented CBIS-DDSM images.

CBAM attention modules focus the model on clinically relevant mammographic regions, reducing false positive rates and improving segmentation specificity without additional inference overhead.

Peer-Reviewed Architecture

Ready to analyse a mammogram?

Upload an image and receive a full AI analysis with segmentation, GradCAM saliency, and a complete clinical PDF report — in seconds.

Get Started Free
No login required REST API available PDF report included Open source