A novel convolution transformer-based network for histopathology-image classification using adaptive convolution and dynamic attention

Renal cell carcinoma (RCC), which is the primary subtype of kidney cancer, is among the leading causes of cancer. Recent breakthroughs in computer vision, particularly deep learning, have revolutionized the analysis of histopathology images, thus providing potential solutions for tasks such as the grading of renal cell carcinoma. Nevertheless, the multitude of available neural network architectures and the absence of systematic evaluations render it challenging to identify optimal models and training configurations for distinct histopathology classification tasks. Hence, we propose a novel hybrid model that effectively combines the advantages of vision transformers and convolutional neural networks. The proposed method, which is named the renal cancer grading network, comprises two essential components: an adaptive convolution (AC) block and a dynamic attention (DA) block. The AC block emphasizes efficient feature extraction and spatial representation learning via intelligently designed convolutional operations. The DA block, which is constructed on the features of the AC block, is a crucial module for histopathology-image classification. It introduces a dynamic attention mechanism and employs a transformer encoder to refine learned representations. Experiments were conducted on four publicly available histopathology datasets: RCC dataset of Kasturba medical college (KMC), colorectal cancer histology (CRCH), break cancer histology (BreakHis) and colon cancer histopathology dataset (CCH). The proposed method demonstrated an accuracy of 90.62%, precision of 91.23%, recall of 90.63%, and a weighted harmonic mean of precision and recall (F1-score) of 90.92 on the KMC dataset. Similarly, the proposed method demonstrates consistent accuracy (weighted average F1-score of 99%) on the CRCH dataset, recognition rate of 88.30% on the BreakHis dataset, and an accuracy of 99.7% on CCH dataset. These results confirm that our method outperforms the state-of-the-art methods, thus demonstrating its effectiveness and robustness across various datasets.