CancerNet-W: A Symmetry-Driven Adaptive Deep Learning Pipeline with Dynamic Learning Rate Control for Early Breast and Cervical Cancer Detection

Malignant lymphoma and other cancer types remain major global health concerns due to their rapid progression and potential for fatal outcomes. Conventional diagnostic approaches are often invasive and time-consuming, contributing to delays in early detection and treatment. These limitations highlight the urgent need for more accurate, efficient, and non-invasive diagnostic solutions that support timely clinical decision-making. In this study, we introduce CancerNet-W, a deep learning (DL) model built upon EfficientNet-B3 for automated classification of breast and cervical cancers using histopathological images (HIs). The model incorporates an Intelligent Learning Rate Controller (ILRC) that adaptively optimizes the LR during training, enhancing stability and performance. The preprocessing pipeline includes data augmentation, resizing, and normalization for the two datasets to improve feature extraction. The Breast cancer HIs Classification (BreakHis) dataset contains 10,000 HIs and the Cervical cancer (SipaKMed) dataset consists of 25,000 images. Importantly, the model leverages morphological cues such as cellular symmetry, which plays a key role in differentiating normal tissue, typically exhibiting more symmetric cellular organization—from malignant tissue, where cancer progression disrupts structural symmetry and leads to notable nuclear and architectural asymmetry, a hallmark of breast and cervical malignancies. This observation aligns with established findings on symmetry breaking in tumorigenesis and nuclear pleomorphism in cancer pathology. CancerNet-W achieved remarkable performance as a general model, yielding 100% accuracy for cervical cancer and 99.89% for breast cancer, outperforming state-of-the-art models including EfficientNet-B4, EfficientNet-B5, DenseNet-201, and MobileNet-V2. To promote strong learning and reduce overfitting, stratified five-fold cross-validation was utilized for the training-validation dataset. Model selection and optimization were based solely on validation performance. An independent test set, kept separate from both training and validation, was employed for final evaluation. The results, accuracy at 98.11% for breast cancer and 99.60% for cervical cancer, reflect the average test performance from the model trained across the five folds. Therefore, the proposed framework provides consistent and dependable diagnostic predictions while significantly reducing the time and cost associated with cancer detection, demonstrating its potential as a valuable tool for clinical applications.