Exploiting deep transfer learning based precise classification and grading of renal cell carcinoma using...

Area of Science:

• Oncology
• Medical Imaging
• Computational Pathology

Background:

• Renal Cell Carcinoma (RCC) is a significant cause of cancer-related mortality in men globally.
• Early diagnosis of RCC is crucial for improving patient survival rates.
• Manual histopathological evaluation of renal tissue is time-consuming, prone to errors, and subject to inter-observer variability.

Purpose of the Study:

• To develop an automated method for precise classification and grading of RCC using histopathological images.
• To enhance the accuracy and reduce bias in RCC diagnosis through automated analysis.
• To investigate the effectiveness of deep transfer learning techniques for RCC detection.

Main Methods:

• A novel method, Exploiting Deep Transfer Learning based Precise Classification and Grading of Renal Cell Carcinoma (EDTL-PCGRCC), was developed.
• The method incorporates Wiener filtering (WF) for noise removal in histopathological images.
• Features were extracted using an improved MobileNetV2 model, and classification was performed using an Elman Neural Network (ENN) optimized by Improved Artificial Ecosystem Optimization (IAEO).

Main Results:

• The EDTL-PCGRCC method demonstrated robust performance in classifying and grading RCC.
• The automated approach showed potential in overcoming the limitations of manual histopathological assessment.
• Empirical findings confirmed the method's effectiveness on a biomedical image dataset.

Conclusions:

• The proposed EDTL-PCGRCC method offers a promising automated solution for accurate RCC classification and grading.
• Deep transfer learning combined with advanced optimization techniques can significantly improve diagnostic efficiency and reliability in renal cancer pathology.
• This approach has the potential to reduce misdiagnosis rates and aid clinicians in better patient management.