| dc.description.abstract | Accurate and interpretable medical image classification remains a critical challenge in
computer-aided diagnosis, particularly under limited dataset conditions. Deep learning
models often struggle to capture both local and global patterns simultaneously, and their
black-box nature limits clinical trust. Incorporating attention mechanisms and explain-
ability techniques can enhance both performance and interpretability. This research
proposes a modified Self-Attention Compact Convolutional Transformer (SA-CCT)
architecture designed to improve feature extraction and classification performance for lung
cancer detection. The model integrates enhanced self-attention mechanisms and
custom MLP blocks within the transformer framework, coupled with an improved patch
based tokenization strategy that captures richer local and global features from grayscale
CT images. To ensure interpretability, Grad-CAM explainability and segmentation
based visualization modules are incorporated that enables spatial localization of
discriminative regions and validation of model focus. The proposed SA-CCT model
is trained and evaluated on a comprehensive lung cancer dataset that achieves 99%
of classification accuracy, along with robust performance across per-class metrics and
confusion analysis. These results show that the modified SA-CCT architecture is a
very successful and easy to understand way to automatically diagnose lung cancer. It
also provides a framework that may be used again and again for future research in
medical image analysis and transformer-based categorization.
Keywords: Lung Cancer Classification, CT Image, SA-CCT, CNN-Transformer Hybrid,
Self-Attention, Explainable AI, Grad-CAM, Convolutional Tokenization, Custom MLP
Block, Sequence Pooling. | en_US |
