Renal Cancer Detection from Histopathological Images Using Deep Learning
Akhil Kumar, R. Krithiga, S. Suseela, B. Swarna, T. Karthikeyan · 2025
Renal cancer stands as a significant contributor to global cancer-related mortality. The imperative for early detection to enhance patient outcomes is evident, yet the manual analysis of histopathological images proves challenging and time-consuming for pathologists. Harnessing the potential of deep learning presents an opportunity to revolutionize renal cancer detection through automated histopathological image analysis. Our research aims to assess the effectiveness of established deep learning models in renal cancer detection. We will curate a collection of representative models previously developed for histopathological image analysis. Subsequently, these models will undergo training using an extensive dataset comprising histopathological images of both renal cancer and normal tissue. The ultimate goal is to evaluate the performance of these pre-trained models on a separate test dataset containing histopathological images of renal cancer and normal tissue. This investigation is poised to furnish valuable insights into the capabilities of existing deep learning models for renal cancer detection. The findings have the potential to guide the development of automated tools that facilitate more accurate and efficient diagnosis of renal cancer by assisting pathologists in their assessments without increasing the word count.