Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer.
The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes nu...
| Published in: | BioMed Research International pp. 1 - 15 |
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| Main Authors: | , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
8/10/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158445008&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158445008 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/10/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158445008 158445008 158445008 10.1155/2022/8616535 158445008 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer. aug: au: Ashokkumar, N. Meera, S. Anandan, P. Murthy, Mantripragada Yaswanth Bhanu Kalaivani, K. S. Alahmadi, Tahani Awad Alharbi, Sulaiman Ali Raghavan, S. S. Jayadhas, S. Arockia affil: Department of Electronics and communication Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andra Pradesh 517102, India sug: subj: Breast Neoplasms Physiopathology Neural Networks (Computer) Lymph Nodes Pathology Neoplasm Metastasis Deep Learning Human Middle Age Aged Confidence Intervals ROC Curve Breast Neoplasms Prognosis Diagnostic Imaging Middle Aged: 45-64 years Aged: 65+ years ab: The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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