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

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 can...

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Main Authors: Rafiqul Islam, Al Imran, Md. Fazle Rabbi
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Prostate Cancer
Online Access:http://dx.doi.org/10.1155/2024/1588891
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author Rafiqul Islam
Al Imran
Md. Fazle Rabbi
author_facet Rafiqul Islam
Al Imran
Md. Fazle Rabbi
author_sort Rafiqul Islam
collection DOAJ
description 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.
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spelling doaj-art-9f6c61fc54a5481ea4080346ac32c12b2025-02-03T05:56:54ZengWileyProstate Cancer2090-312X2024-01-01202410.1155/2024/1588891Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer LearningRafiqul Islam0Al Imran1Md. Fazle Rabbi2Department of IoT and Robotics EngineeringDepartment of Computer Science and EngineeringDepartment of Computer Science and EngineeringProstate 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.http://dx.doi.org/10.1155/2024/1588891
spellingShingle Rafiqul Islam
Al Imran
Md. Fazle Rabbi
Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning
Prostate Cancer
title Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning
title_full Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning
title_fullStr Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning
title_full_unstemmed Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning
title_short Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning
title_sort prostate cancer detection from mri using efficient feature extraction with transfer learning
url http://dx.doi.org/10.1155/2024/1588891
work_keys_str_mv AT rafiqulislam prostatecancerdetectionfrommriusingefficientfeatureextractionwithtransferlearning
AT alimran prostatecancerdetectionfrommriusingefficientfeatureextractionwithtransferlearning
AT mdfazlerabbi prostatecancerdetectionfrommriusingefficientfeatureextractionwithtransferlearning