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Classification and grade prediction of kidney cancer histological images using deep learning

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Abstract

Renal Cell Carcinoma (RCC) is the most common malignant tumor (85%) of kidney cancer and has a complex histological pattern and nuclear structure. The manual diagnosis of kidney cancer or any other cancer from histopathology image depends on the knowledge and experience of pathologists, and the pathologist’s experience influences the results. According to studies, the kind of histology in kidney cancer is related to the prognosis and course of treatment. Since the kind of histology, molecular profile, and stage of the disease all affect how the disease is treated, there is an essential need to develop an automated system that can precisely analyze the histopathological images of the disease. This work demonstrates how a deep learning framework can be used to predict and classify associated grades of RCC from provided haematoxylin and eosin (H &E) images. The proposed model focuses on two important tasks- First to capture and extract associated features from the H &E images of five different grades. Second, to classify the new set of unseen H &E images into five separate grades using the obtained features. The proposed architecture has been tested and experimented on two independent datasets containing H &E stained histopathology images. The proposed architecture has been examined using the following performance metrics namely precision, recall, F1 - score, accuracy, Floating-point operations (FLOPs), and the total number of parameters. The obtained results show that the proposed architecture attains better results over seven state-of-the-art deep learning architectures on two different H &E stained histopathology image datasets.

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Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the handling editor, editor-in-chief, and anonymous reviewers for their valuable suggestions and comments which helped to improve the quality of the research paper.

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Authors and Affiliations

  1. CSE(AIML) Department, Kolhapur Institute of Technology’s College of Engineering (Autonomous), Kolhapur, 416234, Maharashtra, India

    Amit Kumar Chanchal

  2. Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru, 575025, Karnataka, India

    Sravya N, Shyam Lal & Sandeep Kumar

  3. VY Institute of Medical Science, Raipur, Chhattisgarh, India

    PU Prakash Saxena

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Correspondence to Amit Kumar Chanchal or Shyam Lal.

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Chanchal, A.K., N, S., Lal, S. et al. Classification and grade prediction of kidney cancer histological images using deep learning. Multimed Tools Appl 83, 78247–78267 (2024). https://doi.org/10.1007/s11042-024-18639-5

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  • DOI: https://doi.org/10.1007/s11042-024-18639-5

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