RAF2Net: Automated grading of Renal cell Carcinoma utilizing Attention-enhanced deep learning models through Feature Fusion

It is anticipated that the number of instances of kidney cancer will continue to rise globally, which motivates changes to the current diagnostic framework in order to address emerging issues. Renal cell carcinoma (RCC) accounts for 80-85% of all renal tumors and is the most common kind of kidney cancer. Based on kidney histopathology images, this study presented a completely automated, robust, and computationally efficient Renal Cell Carcinoma Grading Network (RAF 2Net). Our suggested model incorporates 3 different Mobilenet backbones with intelligent feature fusion. Moreover, the attention blocks help us give more importance to the important pixels, which are majorly responsible for classification. For comparison purposes, Similar tests were conducted using transfer learning methods with pre-trained ImageNet weights and deep learning models created from scratch. To show the efficacy of the suggested method, we have computed evaluation parameters like Accuracy, Precision, F score, Recall, Confusion Matrix, and TSNE. Based on the provided KMC dataset, the experimental result demonstrates that the proposed RAF 2Net outperforms the nine most recent classification methods regarding prediction Accuracy, Recall, Precision, and F score with a value greater than 92%.