Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning

IF 2.3 Q3 ONCOLOGY
Rafiqul Islam, Al Imran, Md. Fazle Rabbi
{"title":"Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning","authors":"Rafiqul Islam, Al Imran, Md. Fazle Rabbi","doi":"10.1155/2024/1588891","DOIUrl":null,"url":null,"abstract":"Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This 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. The random forest approach then uses these features for classification. The study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their effectiveness in extracting significant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specificity, and accuracy are used to assess the models’ efficacy. 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 offer valuable insights into the decision-making process, thereby addressing a critical problem for clinical practice acceptance. The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. The 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 refine the deep learning models using a small amount of annotated prostate cancer data. The objective of this method is to improve the ability of the models to generalize across different patient populations and clinical situations. This study’s results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the field of diagnosing prostate cancer, when used with random forest’s classification abilities. The results of this work provide a basis for creating reliable and easily understandable machine learning-based diagnostic tools for detecting prostate cancer. This will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identification, and cancer classification.","PeriodicalId":20907,"journal":{"name":"Prostate Cancer","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Prostate Cancer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2024/1588891","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This 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. The random forest approach then uses these features for classification. The study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their effectiveness in extracting significant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specificity, and accuracy are used to assess the models’ efficacy. 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 offer valuable insights into the decision-making process, thereby addressing a critical problem for clinical practice acceptance. The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. The 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 refine the deep learning models using a small amount of annotated prostate cancer data. The objective of this method is to improve the ability of the models to generalize across different patient populations and clinical situations. This study’s results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the field of diagnosing prostate cancer, when used with random forest’s classification abilities. The results of this work provide a basis for creating reliable and easily understandable machine learning-based diagnostic tools for detecting prostate cancer. This will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identification, and cancer classification.
利用迁移学习高效提取特征,从核磁共振成像中检测前列腺癌
前列腺癌是一种对全球健康有重大影响的常见癌症。及时准确的识别对于制定高效的治疗策略和提高患者疗效至关重要。本研究调查了利用机器学习技术诊断前列腺癌的情况。它强调利用深度学习模型(即 VGG16、VGG19、ResNet50 和 ResNet50V2)来提取相关特征。然后,随机森林方法利用这些特征进行分类。研究首先对上述深度学习架构进行了全面的比较检查,以评估它们在从前列腺癌成像数据中提取重要特征方面的有效性。灵敏度、特异性和准确性等关键指标用于评估模型的功效。在识别前列腺癌图像中的重要特征方面,ResNet50 的准确率高达 99.64%,优于其他测试模型。此外,对理解因素的分析旨在为决策过程提供有价值的见解,从而解决临床实践验收中的一个关键问题。随机森林分类器是一种功能强大的集合学习方法,以其适应性和处理复杂数据集的能力而闻名,它将收集到的特征作为输入。随机森林模型旨在识别特征空间中的模式,并对前列腺癌的存在与否做出精确预测。此外,该研究还利用迁移学习方法,使用少量带注释的前列腺癌数据完善深度学习模型,从而解决了数据集可用性受限的问题。这种方法的目的是提高模型在不同患者群体和临床情况下的泛化能力。这项研究的结果非常有用,因为它们显示了 VGG16、VGG19、ResNet50 和 ResNet50V2 与随机森林的分类能力结合使用时,在前列腺癌诊断领域提取特征的效果如何。这项工作的成果为创建可靠、易懂的基于机器学习的前列腺癌诊断工具奠定了基础。这将提高在临床环境中进行早期精确诊断的可能性,如索引术语 "深度学习"、"机器学习"、"前列腺癌"、"癌症识别 "和 "癌症分类"。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Prostate Cancer
Prostate Cancer ONCOLOGY-
CiteScore
2.70
自引率
0.00%
发文量
9
审稿时长
13 weeks
期刊介绍: Prostate Cancer is a peer-reviewed, Open Access journal that provides a multidisciplinary platform for scientists, surgeons, oncologists and clinicians working on prostate cancer. The journal publishes original research articles, review articles, and clinical studies related to the diagnosis, surgery, radiotherapy, drug discovery and medical management of the disease.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信