Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning
Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL...
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MDPI AG
2023
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Online Access: | https://doi.org/10.3390/diagnostics14010095 https://doaj.org/article/c3baed658d6e4449b0a54aa4f40b959a |
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ftdoajarticles:oai:doaj.org/article:c3baed658d6e4449b0a54aa4f40b959a 2024-02-11T10:03:22+01:00 Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning Amandeep Kaur Chetna Kaushal Jasjeet Kaur Sandhu Robertas Damaševičius Neetika Thakur 2023-12-01T00:00:00Z https://doi.org/10.3390/diagnostics14010095 https://doaj.org/article/c3baed658d6e4449b0a54aa4f40b959a EN eng MDPI AG https://www.mdpi.com/2075-4418/14/1/95 https://doaj.org/toc/2075-4418 doi:10.3390/diagnostics14010095 2075-4418 https://doaj.org/article/c3baed658d6e4449b0a54aa4f40b959a Diagnostics, Vol 14, Iss 1, p 95 (2023) breast cancer diagnosis deep mutual learning histopathology imaging diagnosis Medicine (General) R5-920 article 2023 ftdoajarticles https://doi.org/10.3390/diagnostics14010095 2024-01-14T01:39:18Z Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings. Article in Journal/Newspaper DML Directory of Open Access Journals: DOAJ Articles Diagnostics 14 1 95 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
breast cancer diagnosis deep mutual learning histopathology imaging diagnosis Medicine (General) R5-920 |
spellingShingle |
breast cancer diagnosis deep mutual learning histopathology imaging diagnosis Medicine (General) R5-920 Amandeep Kaur Chetna Kaushal Jasjeet Kaur Sandhu Robertas Damaševičius Neetika Thakur Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning |
topic_facet |
breast cancer diagnosis deep mutual learning histopathology imaging diagnosis Medicine (General) R5-920 |
description |
Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings. |
format |
Article in Journal/Newspaper |
author |
Amandeep Kaur Chetna Kaushal Jasjeet Kaur Sandhu Robertas Damaševičius Neetika Thakur |
author_facet |
Amandeep Kaur Chetna Kaushal Jasjeet Kaur Sandhu Robertas Damaševičius Neetika Thakur |
author_sort |
Amandeep Kaur |
title |
Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning |
title_short |
Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning |
title_full |
Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning |
title_fullStr |
Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning |
title_full_unstemmed |
Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning |
title_sort |
histopathological image diagnosis for breast cancer diagnosis based on deep mutual learning |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/diagnostics14010095 https://doaj.org/article/c3baed658d6e4449b0a54aa4f40b959a |
genre |
DML |
genre_facet |
DML |
op_source |
Diagnostics, Vol 14, Iss 1, p 95 (2023) |
op_relation |
https://www.mdpi.com/2075-4418/14/1/95 https://doaj.org/toc/2075-4418 doi:10.3390/diagnostics14010095 2075-4418 https://doaj.org/article/c3baed658d6e4449b0a54aa4f40b959a |
op_doi |
https://doi.org/10.3390/diagnostics14010095 |
container_title |
Diagnostics |
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14 |
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1 |
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95 |
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1790599584001556480 |