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

Summary

An explainable transfer-learning protocol using EfficientNet-B0 and Grad-CAM is developed for classifying breast ultrasound images into benign, malignant, and normal categories, providing interpretable diagnostic heatmaps suitable for clinical decision support applications.

Abstract

Breast cancer identification via ultrasound images requires high accuracy and transparency to assist clinicians in taking appropriate decisions. This work demonstrates a deep learning system for classification of breast ultrasound images into benign, malignant, and normal images using the EfficientNet-B0 architecture fine-tuned on the Breast Ultrasound Identification (BUSI) dataset. To mitigate class imbalance and stabilize the network, data augmentation including random horizontal flipping, rotation, and color jittering is applied. Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to generate visual explanations by identifying regions of interest such as tumor margins and texture patterns. The model achieved an average accuracy of 99%, demonstrating high efficacy in lesion detection. The integration of Explainable AI (XAI) not only improves diagnostic confidence but also bridges the gap between clinical practice and AI. The results prove the potential for combining high-performing deep learning models with interpretability methods in developing reliable breast cancer diagnosis tools suitable for actual clinical practice.

Introduction

Breast cancer is one of the most significant life-threatening diseases affecting women worldwide. According to the World Health Organization (WHO), breast cancer contributes to a majority of cancer-related deaths, and early diagnosis is critical in terms of enhancing the survival rate¹. Ultrasound imaging has become a routine diagnostic procedure for breast cancer because it is safe, inexpensive, and can produce real-time images. As opposed to mammography, ultrasound is extremely effective at differentiating solid masses and growth that contain fluid and, therefore, serves as an excellent diagnostic aid in the presence of breast pathology. Ultr....

Protocol

1. Dataset characteristics

The study utilized the publicly available Breast Ultrasound Images (BUSI) dataset, which provided a well-documented repository for the analysis and classification of breast cancer²⁹. This dataset proved particularly appropriate as it incorporated a vast collection of ultrasound images required to establish and validate deep learning models for medical imaging. The dataset comprised 780 ultrasound images collected from 600 women aged 25–75 years, representing a diverse population for breast cancer diagnosis. All images maintained an average resolution of 500 x 500 pixels and ....

Results

Standardization of input dimensions to 24 X 224 pixels and ImageNet-based normalization facilitated stable training behavior and smooth convergence of the loss curves. The application of the mask-overlay operation on training samples significantly increased the visibility of lesion boundaries compared to raw ultrasound images. This observation supported the hypothesis that anatomically guided enhancement improves feature separability. While the training phase utilized these overlays, the validation and test sets remained.......

Discussion

The proposed framework utilizes a fine-tuned EfficientNet-B0 architecture integrated with Gradient-weighted Class Activation Mapping (Grad-CAM) to provide a high-performance, interpretable system for breast ultrasound classification. This research advances the scientific field by bridging the gap between "black-box" deep learning models and clinical transparency, demonstrating that computational efficiency does not have to be sacrificed for explainability⁴⁰. By implementing lesion-consciou.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Breast Ultrasound Images (BUSI Dataset)		N/A	780 PNG images (487 Benign, 210 Malignant, 133 Normal)
Matplotlib	Matplotlib Developers	N/A	Version 3.7.1
NumPy	NumPy Developers	N/A	Version 1.23.5
NVIDIA Tesla T4 GPU	NVIDIA	N/A	16GB VRAM
OpenCV	OpenCV.org	N/A	Version 4.7.0
Pillow (PIL)	Python Imaging Library	N/A	Version 9.4.0
Python	Python Software Foundation	N/A	Version 3.9.12
PyTorch	Meta AI	N/A	Version 2.0.1+cu117
Scikit-learn	Scikit-learn Developers	N/A	Version 1.2.2
Seaborn	Seaborn Developers	N/A	Version 0.12.2
Ubuntu OS	Canonical	N/A	64-bit