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Kidney cancer diagnosis, a critical facet of oncology, demands accurate and timely detection for effective treatment. This research delved into harnessing the capabilities of deep learning, particularly the MobileNet architecture, to revolutionize kidney cancer detection. A curated dataset was preprocessed and employed to train a MobileNet-based model, achieving exceptional results: F1-score, recall, and precision at 99.89%, and a perfect ROC curve (AUC = 1.000). Swift inference, at 1.5 s per 2000 images, underscored the model’s clinical viability. Comparative analysis highlighted the model’s superiority over traditional methods. These results substantiate the model’s promise as a diagnostic tool. Acknowledging potential limitations and dataset nuances, this research underscores the potential synergy of AI and clinical expertise. Ultimately, this study contributes to the transformative potential of deep learning in enhancing kidney cancer detection and patient outcomes.

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Advances in Kidney Cancer Detection: Harnessing the Power of Deep Learning for Accurate Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Mohammed A. Alkahlout,
  • Samy S. Abu-Naser

摘要

Kidney cancer diagnosis, a critical facet of oncology, demands accurate and timely detection for effective treatment. This research delved into harnessing the capabilities of deep learning, particularly the MobileNet architecture, to revolutionize kidney cancer detection. A curated dataset was preprocessed and employed to train a MobileNet-based model, achieving exceptional results: F1-score, recall, and precision at 99.89%, and a perfect ROC curve (AUC = 1.000). Swift inference, at 1.5 s per 2000 images, underscored the model’s clinical viability. Comparative analysis highlighted the model’s superiority over traditional methods. These results substantiate the model’s promise as a diagnostic tool. Acknowledging potential limitations and dataset nuances, this research underscores the potential synergy of AI and clinical expertise. Ultimately, this study contributes to the transformative potential of deep learning in enhancing kidney cancer detection and patient outcomes.