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An efficient enhanced feature framework for grading of renal cell carcinoma using Histopathological Images

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Abstract

Renal cell carcinoma (RCC) represents the primary type of kidney cancer, responsible for approximately 85% of kidney cancer-related fatalities. Precise grading of this cancer is pivotal for tailoring effective treatments. Detecting RCC early, before metastasis, significantly improves survival rates. While Artificial intelligence-based classification methods have emerged for RCC, advancements in accuracy, processing efficiency, and memory utilization remain imperative. This study introduces the Efficient Enhanced Feature Framework (EFF-Net), a deep neural network architecture designed for RCC grading using histopathological image analysis. EFF-Net amalgamates potent feature extraction from convolutional layers with efficient Separable convolutional layers, aiming to accelerate model inference, reduce trainable parameters, mitigate overfitting, and elevate RCC grading precision. Evaluation across three distinct datasets showcases the EFF-Net's outstanding performance: achieving 91.90% accuracy, a precision of 91.4%, a recall of 91.8%, and a harmonic mean of precision and recall (F1 score) of 91.9% on the Kasturba Medical College (KMC) dataset. Additionally, on the Lung and Colon Dataset, EFF-Net achieved 99.8% accuracy, a precision of 99.7%, a recall of 99.9%, and a 98.7% F1 score. Similarly, the Acute Lymphoblastic Leukaemia dataset demonstrated remarkable performance: 99.8% accuracy, a precision of 99%, a recall of 99%, and a 99.7% F1 score. EFF-Net's superior accuracy surpasses existing state-of-the-art approaches while exhibiting reduced trainable parameters and computational requirements.

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Funding

This work was supported by the National key research and development plan (Grant NO: 2023YFB4502704) and the National Natural Science Foundation of China (Grant NO: 62302461)

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Contributions

Conceptualization, Methodology: Faiqa Maqsood, Formal Analysis, the original draft, Writing—review and editing: Faiqa Maqsood and Muhammad Mumtaz Ali., and Wang Zhenfei. Review, editing, and supervision: Wang Zhenfei., Baozhi Qiu, Tahir Mahmood, and Raheem Sarwar. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zhenfei Wang.

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Maqsood, F., Wang, Z., Ali, M.M. et al. An efficient enhanced feature framework for grading of renal cell carcinoma using Histopathological Images. Appl Intell 55, 196 (2025). https://doi.org/10.1007/s10489-024-06047-z

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