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Performance is evaluated using sensitivity, specificity, and accuracy, with reported results showing ResNet50 achieving 99.64%. 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[https://doi.org/10.1155/2024/1588891](https://doi.org/10.1155/2024/1588891)  \nReview Article  \nProstate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning  \nRafqul Islam , 1 Al Imran,2 and Md. Fazle Rabbi2  \n1 Department of IoTand Robotics Engineering, Bangabandhu Sheikh Mujibur Rahman Digital University, Gazipur, Bangladesh 2 Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh  \nCorrespondence should be addressed to Rafqul Islam; [rafqul.cse.jnu@gmail.com](rafqul.cse.jnu@gmail.com)  \nReceived 18 March 2024; Revised 19 April 2024; Accepted 26 April 2024; Published 16 May 2024  \nAcademic Editor: Mohd Farhan  \nCopyright © 2024 Rafqul Islam et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nProstate cancer is a common cancer with signifcant implications for global health. Prompt and precise identifcation is crucial forefcient treatment strategizing and enhanced patient results. Tis research study investigates the utilization of machine learning techniques to diagnose prostate cancer. It emphasizes utilizing deep learning models, namely VGG16, VGG19, ResNet50, and ResNet50V2, to extract relevant features. Te random forest approach then uses these features for classifcation. Te study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their efectiveness in extracting signifcant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specifcity, and accuracy are used to assess the models’ efcacy. With an accuracy of 99.64%, ResNet50 outperformed other tested models when it came to identifying important features in images of prostate cancer. Furthermore, the analysis of understanding factors aims to ofer valuable insights into the decision-making process, thereby addressing a critical problem for clinical practice acceptance. Te random forest classifer, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. Te random forest model seeks to identify patterns in the feature space and produce precise predictions on the presence or absence of prostate cancer. In addition, the study tackles the restricted availability of datasets by utilizing transfer learning methods to refne the deep learning models using a small amount of annotated prostate cancer data. Te objective of this method is to improve the ability of the models to generalize across diferent patient populations and clinical situations. Tis study’s results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the feld of diagnosing prostate cancer, when used with random forest’s classifcation abilities. Te results of this work provide a basis for creating reliable and easily understandable machine learning-based diagnostic tools for detecting prostate cancer. Tis will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identifcation, and cancer classifcation.  \n1. Introduction  \nInnovative and precise diagnostic procedures are needed to improve patient outcomes for prostate cancer, the second most frequent malignancy in men worldwide. Prostate cancer identifcation must be nuanced because of its wide range of clinical manifestations and disease progression. While useful, traditional diagnostic procedures struggle to achieve the precision needed for prompt action. Using supervised machine learning algorithms to detect prostate cancer could improve diagnostic accuracy and clinical decision-making. Machine learning has revol","cbCaiib4NJ8U9HAR","https://ap.wps.com/l/cbCaiib4NJ8U9HAR","pdf",3135719,28,"English","# Introduction\n# Prostate Cancer","[{\"question\":\"Which machine learning and deep learning methods are used for prostate cancer detection in the study?\",\"answer\":\"The approach uses deep learning feature extractors including VGG16, VGG19, ResNet50, and ResNet50V2, and then applies a random forest classifier on the extracted features.\"},{\"question\":\"How is model performance evaluated in the review?\",\"answer\":\"The review describes evaluation using key metrics such as sensitivity, specificity, and accuracy to assess model effectiveness for prostate cancer imaging data.\"},{\"question\":\"How does the review address the limited availability of datasets?\",\"answer\":\"It discusses using transfer learning to refine deep learning models with a small amount of annotated prostate cancer data, improving generalization across patient populations and clinical settings.\"}]","Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning - Review Article | PDF",1790020539,71]