Driven early detection of chronic kidney cancer disease based on machine learning technique.
Journal:
PloS one
Published Date:
Jul 15, 2025
Abstract
In recent times, chronic kidney cancer has been considered a significant cause of cancer, and Renal Cell Carcinoma (RCC) has become a significant prevalent among various kidney cancer conditions. The analysis of kidney cancer, an important and often time-sensitive medical task, has seen a breakthrough alteration by incorporating deep learning (DL) methods, mainly in analyzing histopathological images (HIs). Given manual analysis's inherent complexity and time-consuming aspect, automatic systems leveraging DL methods provide a promising solution. Automated techniques powered by DL methods showcase a notable capability to analyze intricate details within HIs. These methods are adept at recognizing complex patterns and anomalies within HIs, accelerating the diagnostic method and increasing accuracy. The combination of advanced computational methods with the assessment of kidney cancer HIs not only overcomes the demanding requirements for timely identification but also paves the way for more effective and reliable diagnostic processes in renal oncology. This study presents the Kidney Cancer Detection and Classification employing a Snake Optimizer with Deep Learning on Pathological Images (KCDC-SODLPI) technique. The main aim of the KCDC-SODLPI method is to analyze the pathological images to determine the presence of kidney cancer. In the multifaceted process, the KCDC-SODLPI technique utilizes a Gaussian filtering (GF)-based image preprocessing approach to eliminate the noise content. Furthermore, the KCDC-SODLPI method employs the SE-DenseNet model for extracting intricate patterns from the input images. Moreover, the SO model is used to tune the hyperparameter of the SE-DenseNet method. Finally, the bidirectional long shortterm memory (BiLSTM) model is implemented to detect and classify kidney cancer. The performance of the KCDC-SODLPI technique is evaluated under the biomedical image dataset. The experimental validation of the KCDC-SODLPI method portrayed a superior accuracy value of 88.90% over existing models.
