Light-PSO-Net: A Novel Hybrid Framework for Breast Cancer Histopathology Classification
Histopathological imaging of breast cancer remains a vital clinical task. While deep learning techniques have shown strong diagnostic accuracy, most existing models suffer from poor interpretability, high computational demands, and limited clinical transparency. Therefore, this research aims to develop a simple, streamlined, and interpretable hybrid system for early, accurate, and reliable breast cancer detection to support real-time clinical diagnosis. A new hybrid architecture, Light-PSO-Net, was proposed by integrating a lightweight convolutional neural network (MobileNetV2), Particle Swarm Optimisation (PSO) for global hyperparameter optimisation, Principal Component Analysis (PCA) for dimensionality reduction, and a Fuzzy Inference System (FIS) to stratify risks in an interpretable manner. The publicly available BreaKHis dataset of breast cancer histopathology images at multiple magnifications, comprising benign and malignant tissues, was utilised to train and test the model. GradCAM visualisation highlighted spatial features used for classification, while PCA-FIS offered semantic interpretability of the results. Model performance was evaluated using accuracy, F1- score, confusion matrices, and ablation analyses. The proposed framework achieved 95.5% classification accuracy and an F1 score of 0.9682, outperforming many other modern lightweight and hybrid deep learning systems. The ablation analysis showed that PSO outperformed PCA in terms of convergence stability, predictive performance, and feature compactness and separability. The fuzzy reasoning module enhanced sensitivity to malignancy and provided categorisation of risk into low, medium, and high risk. The model also demonstrated consistent results across different magnifications and external validation conditions. The combination of global maximisation, dimension minimisation, and rule-based solutions helped the proposed model overcome key challenges in breast cancer diagnosis, such as computational efficiency, interpretability, and clinical relevance. Unlike traditional black-box models, Light-PSO-Net provides both pixel-level and decision-level explainability using GradCAM and fuzzy logic. Its lightweight design makes it suitable for resource-limited pathology laboratories and edge healthcare environments. These findings emphasise the importance of integrating deep learning with explainable and optimisation-based systems for translational medical applications. Light-PSO-Net provides a genuine, effective, and interpretable approach for classifying histopathological breast cancer. The proposed hybrid learn-then-reason pipeline shows strong potential to assist pathologists in early diagnosis and improve clinical outcomes. Future research will focus on multimodal integration, federated learning, adaptive fuzzy systems, and multi-centre validation to enhance generalisability and support wider clinical adoption.