SAWL-Net: A Statistical Attentions and Wavelet Aided Lightweight Network for Classification of Cancers in Histopathological Images

Abstract

Addressing the formidable challenges posed by the diagnosis and management of various types of cancer, including breast, colon, lung, and colorectal cancer, demands innovative solutions to streamline histopathological analysis processes. In this study, we propose a novel lightweight convolutional neural network (CNN) called statistical attentions and wavelet aided lightweight network (SAWL-Net) architecture based on MobileNetV2 equipped with hybrid statistical similarity and wave format-aided attention mechanisms, specifically tailored to the demands of cancer histopathology. By leveraging the capabilities, our model incorporates a lightweight design while ensuring high-performance outcomes. We introduce a unique combination of Pearson correlation coefficient, Spearman rank correlation, and cosine similarity metrics, alongside a specialized wave conversion technique to enhance the detection of similarities across different channels of histopathological data, while providing a holistic approach to the model. In this study, we have considered breast, colorectal, and lung & colon cancer datasets for experimentation. Notably, our model surpasses prevailing state-of-the-art methodologies, showcasing its efficacy in optimizing diagnostic accuracy and expediting treatment strategies for varied cancer types. Our codes are publicly available at the GitHub repository.