Abstract
This study introduces a novel deep learning approach aimed at accurately classifying Gallbladder Cancer (GBC) into benign, malignant, and normal categories using ultrasound images from the challenging GBC USG (GBCU) dataset. The proposed methodology enhances image quality and specifies gallbladder wall boundaries by employing sophisticated image processing techniques like median filtering and contrast-limited adaptive histogram equalization. Unlike traditional convolutional neural networks, which struggle with complex spatial patterns, the proposed transformer-based model, GBC Horizontal-Vertical Transformer (GBCHV), incorporates a GBCHV-Trans block with self-attention mechanisms. In order to make the model anatomy-aware, the square-shaped input patches of the transformer are transformed into horizontal and vertical strips to obtain distinctive spatial relationships within gallbladder tissues. The novelty of this model lies in its anatomy-aware mechanism, which employs horizontal-vertical strip transformations to depict spatial relationships and complex anatomical features of the gallbladder more accurately. The proposed model achieved an overall diagnostic accuracy of 96.21% by performing an ablation study. A performance comparison between the proposed model and seven transfer learning models is further conducted, where the proposed model consistently outperformed the transfer learning models, showcasing its superior accuracy and robustness. Moreover, the decision-making process of the proposed model is further explained visually through the utilization of Gradient-weighted Class Activation Mapping (Grad-CAM). With the integration of advanced deep learning and image processing techniques, the GBCHV-Trans model offers a promising solution for precise and early-stage classification of GBC, surpassing conventional methods with superior accuracy and diagnostic efficacy.