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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/14/1/95 |
| _version_ | 1827384875156504576 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-08T15:09:44Z |
| format | Article |
| id | doaj.art-c3baed658d6e4449b0a54aa4f40b959a |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-08T15:09:44Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c3baed658d6e4449b0a54aa4f40b959a2024-01-10T14:53:58ZengMDPI AGDiagnostics2075-44182023-12-011419510.3390/diagnostics14010095Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual LearningAmandeep Kaur0Chetna Kaushal1Jasjeet Kaur Sandhu2Robertas Damaševičius3Neetika Thakur4Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, IndiaDepartment of Applied Informatics, Vytautas Magnus University, 53361 Akademija, LithuaniaJunior Laboratory Technician, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, IndiaEvery 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.https://www.mdpi.com/2075-4418/14/1/95breast cancer diagnosisdeep mutual learninghistopathology imaging diagnosis |
| spellingShingle | Amandeep Kaur Chetna Kaushal Jasjeet Kaur Sandhu Robertas Damaševičius Neetika Thakur Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning Diagnostics breast cancer diagnosis deep mutual learning histopathology imaging diagnosis |
| title | 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_short | 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 |
| topic | breast cancer diagnosis deep mutual learning histopathology imaging diagnosis |
| url | https://www.mdpi.com/2075-4418/14/1/95 |
| work_keys_str_mv | AT amandeepkaur histopathologicalimagediagnosisforbreastcancerdiagnosisbasedondeepmutuallearning AT chetnakaushal histopathologicalimagediagnosisforbreastcancerdiagnosisbasedondeepmutuallearning AT jasjeetkaursandhu histopathologicalimagediagnosisforbreastcancerdiagnosisbasedondeepmutuallearning AT robertasdamasevicius histopathologicalimagediagnosisforbreastcancerdiagnosisbasedondeepmutuallearning AT neetikathakur histopathologicalimagediagnosisforbreastcancerdiagnosisbasedondeepmutuallearning |