2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 418-433, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ashrf Althbiti 1 *
Doi: https://doi.org/10.54216/FPA.190230
Kidney cancer is a lethal cancerous and very dangerous disease caused by genetic renal disease or by kidney tumors, and some patients might survive since there is no technique for earlier diagnosis of kidney tumor. Earlier diagnosis of kidney tumor assists physicians to begin proper treatment and therapy for the patient, which prevent kidney cancers and renal transplantation. Accurate classification of kidney tumor is vital for prediction and treatment planning. However, manual classification by pathologists could be subjective and time-consuming, and there can be considerable inter-observer variability. With the development of artificial intelligence (AI), automated tools enabled by machine learning (ML) and deep learning (DL) methods could predict cancers. This study designs a new white shark optimizer with an ensemble majority voting based kidney cancer classification (WSO-EMVKCC) technique on pathology images. The presented WSO-EMVKCC technique intends to identify the different grades of kidney cancer using DL and ensemble voting concepts. To accomplish this, the presented WSO-EMVKCC technique employs a deep convolutional neural network based Xception technique for the feature extraction process. Moreover, the WSO model has been used for the optimal hyperparameter tuning of the Xception approach. Furthermore, an ensemble majority voting classifier including three ML techniques like long short-term memory (LSTM), sparse autoencoder (SAE), and gated recurrent unit (GRU) models are employed for kidney cancer classification. The stimulation validation of the WSO-EMVKCC model is performed on the open access histology image database from Kaggle repository. The stimulated values illustrate the promising performance of the WSO-EMVKCC algorithm over other DL techniques.
Kidney cancer classification , Deep learning , Ensemble models , Majority voting , White shark optimizer
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