Automatic Segmentation of Calcification Areas in Digital Breast Images.
In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mamm...
| Publicado en: | BioMed Research International pp. 1 - 8 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/3/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157217064&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157217064 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/3/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157217064 157217064 157217064 10.1155/2022/2525433 157217064 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Segmentation of Calcification Areas in Digital Breast Images. aug: au: Abdulrazzaq, Ammar Akram Muhammed, Yasser Al-Douri, Asaad T. Mohamad, A. A. Hamad Ibrahim, Abdelrahman Mohamed affil: Department of Medical Laboratory Technologies, Al-Maarif University College, Iraq sug: subj: Automation Calcinosis Diagnosis Mammography Digital Imaging Methods Breast Neoplasms Diagnosis Early Detection of Cancer Human Machine Learning Algorithms ab: In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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