Prostate Cancer Detection Using Deep Learning and Traditional Techniques

Abstract

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.