利用深度学习和传统技术检测前列腺癌

Shreyash Matte, Sairaj Mengal, Tanmay Jadhav, Prafull Jadhav, Poorab Khawale, Atharva Khachane, Dattatray G. Takale
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引用次数: 0

摘要

在全球范围内,男性都受到前列腺癌的影响,这是一种既常见又有可能致命的疾病。及时准确的检测对于成功治疗患者并改善其预后至关重要。机器学习技术是人工智能的一个子领域,最近它的出现改变了前列腺癌识别的方式。这项工作旨在全面概述和分析机器学习方法在前列腺癌检测、诊断和预后中的应用。本研究建议使用多种数据集,其中包括遗传信息、临床记录和医学照片。为了保证数据的质量,使用了预处理技术和特征提取技术,以帮助提取相关信息来构建模型。目前正在研究几种不同的机器学习算法,以确定它们是否能有效识别前列腺癌。这些技术包括支持向量机(SVM)、卷积神经网络(CNN)和深度学习架构。在我们的方法过程的整个训练、验证和评估阶段,都会考虑到一些性能指标,包括准确度、精确度、召回率、F1-分数和 ROC-AUC。此外,研究还涉及数据保护、公平性和模型可解释性等伦理方面,这些对于在医疗保健环境中使用机器学习解决方案至关重要。这些研究结果证明,机器学习有可能改善前列腺癌的检测,从而实现更早的诊断和更个性化的治疗方案。此外,理解模型预测的能力和模型的开放性也有助于医疗保健专业人员做出有根据的判断。这项研究为将机器学习融入临床实践提供了真知灼见,从而为不断变化的前列腺癌诊断环境做出了贡献。这反过来又会最终改善患者护理和治疗效果。为了进一步推动前列腺癌诊断和治疗的发展,未来的方法包括不断开发模型、实施更大规模的临床试验以及利用不断发展的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prostate Cancer Detection Using Deep Learning and Traditional Techniques
Worldwide, men are affected by prostate cancer, which is a condition that is both common and has the potential to be fatal. Detection that is both timely and accurate is of the utmost importance for successfully treating patients and improving their outcomes. The technique of machine learning, which is a subfield of artificial intelligence, has recently emerged as a game-changing instrument for the identification of prostate cancer. The purpose of this work is to provide a complete overview and analysis of the use of machine learning methods in the detection, diagnosis, and prognosis of prostate cancer. The study that is being suggested makes use of a wide variety of datasets, which include genetic information, clinical records, and medical photographs. To guarantee the quality of the data, preprocessing techniques are used, and feature extraction techniques are utilized to assist the extraction of relevant information for the construction of models. There are several different machine learning algorithms that are being investigated to see whether they are effective in the identification of prostate cancer. These techniques include support vector machines (SVMs), convolutional neural networks (CNNs), and deep learning architectures. Several performance indicators, including accuracy, precision, recall, F1-score, and ROC-AUC, are taken into consideration throughout the training, validation, and assessment phases of our approach processes. In addition, the research covers ethical aspects, such as data protection, fairness, and the interpretability of models, which are essential for the use of machine learning solutions in healthcare settings. These findings provide evidence that machine learning has the potential to improve prostate cancer detection, which would allow for earlier diagnosis and more individualized therapy courses of treatment. In addition, the capacity to comprehend the predictions of the model and the openness of the model facilitate the ability of healthcare professionals to make educated judgements. This study contributes to the ever-changing environment of prostate cancer diagnosis by providing insights into the incorporation of machine learning into clinical practice. This, in turn, eventually leads to improvements in patient care and outcomes. To further advancing prostate cancer diagnosis and therapy, future approaches include the continuous development of models, the implementation of larger-scale clinical trials, and the utilization of developing technology respectively.
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