A Study on Bladder Cancer Detection using AI-based Learning Techniques

Apeksha Koul, Yogesh Kumar, Anish Gupta
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引用次数: 1

Abstract

Bladder cancer is currently the most frequent and worst cancer in the United States. Over the last several decades, bladder cancer detection and therapy breakthroughs have significantly reduced its mortality. Cystoscopy treatment has been considered useful for detecting and treating bladder cancer (BCa), but it is also prone to certain complications. Hence, this study has explored numerous research methodologies for identifying and diagnosing bladder cancer using AI techniques such as machine learning and deep learning models. The paper also emphasizes the accomplishments and challenges of researchers in this field. The assessment of the various techniques has also been compared to draw some conclusions.
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基于人工智能学习技术的膀胱癌检测研究
膀胱癌是目前美国最常见和最严重的癌症。在过去的几十年里,膀胱癌的检测和治疗的突破大大降低了其死亡率。膀胱镜检查被认为是检测和治疗膀胱癌(BCa)的有效方法,但它也容易产生某些并发症。因此,本研究探索了利用机器学习和深度学习模型等人工智能技术识别和诊断膀胱癌的多种研究方法。文章还强调了该领域研究人员所取得的成就和面临的挑战。对各种技术的评估也进行了比较,得出了一些结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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