Over the years, Convolutional Neural Networks (CNNs) have demonstrated strong capability in cancer detection and classification using medical images. However, CNN-based models often struggle to capture long-range contextual dependencies. In such scenarios, integrating Compact Convolutional Transformer (CCT) architectures after the CCT layer allows CNN-extracted features to reshape into compact patch tokens using a CCT tokenizer, followed by the addition of positional embeddings to preserve spatial structure. Using 5-fold cross-validation, the model was tested on 3 sets of breast cancer mammography. With only 250,435 parameters, the model achieved 99%-100% accuracy across 3 datasets, indicating robust generalization. Explainable AI (XAI) was integrated into the model to explain the breast cancer classification process to enhance clinical trust. The results indicate that the proposed framework is suitable for computer-aided diagnosis systems, particularly in resource-constrained clinical environments. The novelty of the proposed CNN-integrated CCT overcomes the limitation of CNN's gradient degradation in the last layers by integrating convolutional tokenization with transformer-based learning. Lighter than ViT, which is effective in capturing long-range dependencies, the model has also proven efficient in breast cancer classification by capturing long-range dependencies among breast tissue regions.
Lung and colon cancer (LCC), the second leading cause of death and illness worldwide, is often diagnosed at advanced stages because early symptoms are subtle or absent. Late-stage detection reduces treatment effectiveness and increases the chance of mortality. However, deep learning models, such as the Compact Convolutional Transformer (CCT), have emerged as effective methods for detecting and classifying LCC histopathological images. Histopathological image analysis is challenging due to tissue heterogeneity, staining variability, and class imbalance. CCT has challenges such as high data requirements, computational complexity, and limited interpretability of how global attention patterns influence final classification decisions. Additionally, past research has been criticized for relying on a small number of experiments in CLC. To address these challenges, we develop an optimized CCT (oCCT) that addresses Computational and Architectural Complexity, as well as Local and Global Feature Learning. oCCT was applied to a publicly available CLC dataset with 5 classes and 25,000 histopathological images. The oCCT performance was compared with state-of-the-art CNN architectures, transformer-based networks such as Vision Transformer (ViT) and Swin Transformer, and the original CCT. Among the evaluated models, the oCCT model reliably classifies five histopathological tissue types, achieving 98–99% overall accuracy and over 95% for precision, recall, and F1-score across all classes. This remarkable accuracy underscores the model’s capacity to minimize information loss during processing, a common issue in conventional CNNs. Seven extensive ablation studies were conducted to realize the compactness of oCCT. Since CCT is criticized for functioning largely as “black boxes,” we integrated explainable artificial intelligence to make oCCT easier for clinicians to trust and deploy, with clear explanations of its predictions.
