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) 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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
|---|---|
| Enthalten in: |
Diagnostics (Basel, Switzerland) - 14(2023), 1 vom: 31. Dez. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Kaur, Amandeep [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Breast cancer diagnosis |
|---|
| Anmerkungen: |
Date Revised 21.01.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3390/diagnostics14010095 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM366919954 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM366919954 | ||
| 003 | DE-627 | ||
| 005 | 20240121231921.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240114s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/diagnostics14010095 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1266.xml |
| 035 | |a (DE-627)NLM366919954 | ||
| 035 | |a (NLM)38201406 | ||
| 035 | |a (PII)95 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Kaur, Amandeep |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 21.01.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a breast cancer diagnosis | |
| 650 | 4 | |a deep mutual learning | |
| 650 | 4 | |a histopathology imaging diagnosis | |
| 700 | 1 | |a Kaushal, Chetna |e verfasserin |4 aut | |
| 700 | 1 | |a Sandhu, Jasjeet Kaur |e verfasserin |4 aut | |
| 700 | 1 | |a Damaševičius, Robertas |e verfasserin |4 aut | |
| 700 | 1 | |a Thakur, Neetika |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Diagnostics (Basel, Switzerland) |d 2011 |g 14(2023), 1 vom: 31. Dez. |w (DE-627)NLM250111136 |x 2075-4418 |7 nnns |
| 773 | 1 | 8 | |g volume:14 |g year:2023 |g number:1 |g day:31 |g month:12 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/diagnostics14010095 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 14 |j 2023 |e 1 |b 31 |c 12 | ||