Bridging Accuracy and Interpretability: Deep Learning with XAI for Breast Cancer Detection

TL;DR

This paper introduces an interpretable deep learning framework for breast cancer detection that achieves high accuracy and incorporates explainability techniques to support clinical decision-making.

Contribution

It presents a novel deep learning model combined with XAI methods like SHAP and LIME to enhance interpretability without sacrificing performance.

Findings

Deep neural network achieved 0.992 accuracy and 0.988 F1 score.

Model outperformed traditional algorithms on the same datasets.

Feature importance analysis identified concave points as most influential.

Abstract

In this study, we present an interpretable deep learning framework for the early detection of breast cancer using quantitative features extracted from digitized fine needle aspirate (FNA) images of breast masses. Our deep neural network, using ReLU activations, the Adam optimizer, and a binary cross-entropy loss, delivers state-of-the-art classification performance, achieving an accuracy of 0.992, precision of 1.000, recall of 0.977, and an F1 score of 0.988. These results substantially exceed the benchmarks reported in the literature. We evaluated the model under identical protocols against a suite of well-established algorithms (logistic regression, decision trees, random forests, stochastic gradient descent, K-nearest neighbors, and XGBoost) and found the deep model consistently superior on the same metrics. Recognizing that high predictive accuracy alone is insufficient for clinical adoption due to the black-box nature of deep learning models, we incorporated model-agnostic Explainable AI techniques such as SHAP and LIME to produce feature-level attributions and human-readable visualizations. These explanations quantify the contribution of each feature to individual predictions, support error analysis, and increase clinician trust, thus bridging the gap between performance and interpretability for real-world clinical use. The concave points feature of the cell nuclei is found to be the most influential feature positively impacting the classification task. This insight can be very helpful in improving the diagnosis and treatment of breast cancer by highlighting the key characteristics of breast tumor.