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An Explainable AI-Based Transfer Learning Method for Breast Cancer Prediction.

P Rajeswari¹, Surbhi Bhatia Khan², Prasad P S³

¹Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram campus.

Journal of Visualized Experiments : Jove

|July 6, 2026

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This summary is machine-generated.

This study presents a deep learning system for accurate breast cancer identification from ultrasound images. Explainable AI enhances diagnostic confidence for reliable clinical decision-making.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate breast cancer identification from ultrasound images is crucial for clinical decision-making.
Deep learning offers potential for automated analysis of medical images.
Transparency and interpretability are key for AI adoption in healthcare.

Purpose of the Study:

To develop and evaluate a deep learning system for classifying breast ultrasound images.
To enhance the transparency and interpretability of the deep learning model.
To assess the model's efficacy in assisting clinical diagnosis of breast cancer.

Main Methods:

Utilized EfficientNet-B0 architecture fine-tuned on the Breast Ultrasound Identification (BUSI) dataset.
Applied data augmentation techniques (flipping, rotation, color jittering) to address class imbalance.

Related Experiment Videos

Employed Gradient-weighted Class Activation Mapping (Grad-CAM) for visual explanations and region identification.

Main Results:

Achieved an average accuracy of 99% in classifying breast ultrasound images.
Demonstrated high efficacy in lesion detection and classification (benign, malignant, normal).
Grad-CAM successfully identified regions of interest, such as tumor margins and texture patterns.

Conclusions:

The deep learning system shows high potential for reliable breast cancer diagnosis.
Explainable AI (XAI) integration improves diagnostic confidence and clinical applicability.
The combination of deep learning and XAI is suitable for clinical practice.