An Explainable Detection and Classification of Breast Cancer Using a Novel Deep Learning Model by Using LIME on Histopathological Images

Abstract

Breast cancer (BC) is the most common disease among women globally and a major contributor to both illness and death. The timely and precise detection and classification (DAC) of this life-threatening disease are vital in reducing mortality rates and preventing further complications. Traditional BC detection methods are often time-consuming and costly. This research proposes a Deep Explainable Breast Cancer Detection and Classification Network (DeepEBCDCNet) to enable precise and early diagnosis of BC using histopathological images (HI). The DeepEBCDCNet model consists of 13 learnable layers, including nine convolutional layers (CN) followed by four fully connected (FC) layers. Additionally, the architecture incorporates one input layer, eight leaky ReLU (LR) layers, four ReLU layers, five max pooling layers (MPL), six batch normalization (BN) layers, one cross-channel normalization (CLN) layer, three dropout layers (DL), one softmax layer (SL), and one classification layer (CL). To enhance transparency and interpretability, the Local Interpretable Model-Agnostic Explanations (LIME) method is integrated to describe the model's predictions. The DeepEBCDCNet model is assessed using two BC image datasets: BC (for detection) and Breast Cancer Histology Images (BACH) (for classification). A 10-fold cross-validation method ensures the reliability of the results. The model's performance is compared with state-of-the-art hybrid approaches to assess its effectiveness in BC DAC. The model attained an accuracy of 98.53% in BC detection, while in BC classification (three-class: Benign [B], In situ [IS], and Invasive Carcinoma [IC]), it achieved 98.33% accuracy. The proposed DeepEBCDCNet significantly reduces incorrect diagnoses and enhances classification accuracy, offering a reliable second opinion for pathologists in BC DAC.