Advanced transformer with attention-based neural network framework for precise renal cell carcinoma detection using histological kidney images

Renal cell carcinoma (RCC) is one of the typical categories of kidney cancer and is a varied group of malignancies arising from epithelial cells of the kidney parenchyma. RCC has more than ten subtypes. Classification of RCC sub-types is mainly according to morphologic features seen on histopathological hematoxylin and eosin (H & E)-stained slides. The histology classification of RCCs is of great significance, considering the important therapeutic and prognostic implications of its histologic subtypes. Imaging models play a prominent role in the diagnosis, follow-up, and staging of RCC. Histopathological images comprise morphological markers of disease development that have both predictive and diagnostic value. Recently, deep learning (DL) has achieved advanced performance in various computer vision tasks, including segmentation, image classification, and object detection. With the provision of sufficient data, the precision of a DL-enabled diagnosis model frequently matches or even exceeds that of qualified doctors. This paper presents an Advanced Transformer and Attention-Based Neural Network Framework for the Intelligent Detection of Renal Cell Carcinoma (ATANNF-IDRCC) model. The aim is to develop an accurate and automated model for detecting and ranking RCC using kidney histopathology images. Initially, the image pre-processing stage utilizes the contrast enhancement method to enhance the image quality. Furthermore, the ATANNF-IDRCC model utilizes the Twins-Spatially Separable Vision Transformer (Twins-SVT) method for feature extraction. For the RCC classification process, a hybrid model of bidirectional temporal convolutional network and long short-term memory with an attention mechanism (BiTCN-BiLSTM-AM) is employed. The performance analysis of the ATANNF-IDRCC technique is examined under the RCCGNet dataset. The comparison study of the ATANNF-IDRCC technique demonstrated a superior accuracy value of 98.26% compared to existing models.