BreastHistoNet: A Efficient Breast Cancer Histopathological Image Classification Using Multiscale Features and Channel Recalibration

Abstract

Automatic classification of breast cancer (BrCan) histopathological images is crucial for aiding BrCan diagnosis. Convolutional neural networks often emphasize semantics, however, they face challenges like high computational cost, high memory usage, and difficulty in capturing multiscale features, making them less suitable for resource-constrained clinical applications. Many past researchers have proposed CNN-based deep learning model for BrCan classification. Though these models achieved good classification performance, they are heavy regarding parameter counts, FLOPS, and model size. This paper presents a lightweight model with performance comparable to state-of-the-art methods. It integrates Depthwise- Dilated-Multiscale-Pointwise (DDMP) blocks, Discrete Wavelet Transform (DWT), and Squeeze-and-Excitation (SE) blocks to capture low-level and high-level discriminative features. The DDMP blocks efficiently extract multiscale features using depthwise convolution, multiscale dilated convolutions, and pointwise convolutions. This is followed by dual-stream architecture combining the LL subband of DWT with max-pooling output. These features are then recalibrated using SE blocks to highlight the most significant features. The proposed model consists of two DDMP-SE blocks, followed by Global Average Pooling, dense layers with GELU activation, and a final softmax layer for binary classification. An ablation study further highlights the impact of epochs, activation functions, and batch sizes. BreastHistoNet outperforms other baseline models in terms of model size (7.47 MB), parameter count (0.63 M), and FLOPS (6.50 G). Experimental results on BreaKHis dataset obtain 95.48% accuracy, 95.61% precision, 95.46% specificity, 95.46% recall, and 95.48% F1-score. The performance of BreastHistoNet offers high accuracy while maintaining low computational complexity and minimal memory usage makes it a valuable tool for accurate and efficient BrCan classification.