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A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI
Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666634/ https://www.ncbi.nlm.nih.gov/pubmed/38023149 http://dx.doi.org/10.3389/fonc.2023.1225490 |
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author | Mehmood, Mubashar Abbasi, Sadam Hussain Aurangzeb, Khursheed Majeed, Muhammad Faran Anwar, Muhammad Shahid Alhussein, Musaed |
author_facet | Mehmood, Mubashar Abbasi, Sadam Hussain Aurangzeb, Khursheed Majeed, Muhammad Faran Anwar, Muhammad Shahid Alhussein, Musaed |
author_sort | Mehmood, Mubashar |
collection | PubMed |
description | Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model’s ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer. |
format | Online Article Text |
id | pubmed-10666634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106666342023-01-01 A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI Mehmood, Mubashar Abbasi, Sadam Hussain Aurangzeb, Khursheed Majeed, Muhammad Faran Anwar, Muhammad Shahid Alhussein, Musaed Front Oncol Oncology Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model’s ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer. Frontiers Media S.A. 2023-11-09 /pmc/articles/PMC10666634/ /pubmed/38023149 http://dx.doi.org/10.3389/fonc.2023.1225490 Text en Copyright © 2023 Mehmood, Abbasi, Aurangzeb, Majeed, Anwar and Alhussein https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Mehmood, Mubashar Abbasi, Sadam Hussain Aurangzeb, Khursheed Majeed, Muhammad Faran Anwar, Muhammad Shahid Alhussein, Musaed A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI |
title | A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI |
title_full | A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI |
title_fullStr | A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI |
title_full_unstemmed | A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI |
title_short | A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI |
title_sort | classifier model for prostate cancer diagnosis using cnns and transfer learning with multi-parametric mri |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666634/ https://www.ncbi.nlm.nih.gov/pubmed/38023149 http://dx.doi.org/10.3389/fonc.2023.1225490 |
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