Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.
Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prosta...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/18/2022
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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=158037736&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158037736 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/18/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158037736 158037736 158037736 10.1155/2022/9112587 158037736 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network. aug: au: Tyagi, Shobha Tyagi, Neha Choudhury, Amarendranath Gupta, Gauri Zahra, Musaddak Maher Abdul Rahin, Saima Ahmed affil: Computer Science & Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, 121001 Haryana, India sug: subj: Prostatic Neoplasms Diagnosis Prostatic Neoplasms Classification Cancer Screening Methods Neural Networks (Computer) Human Female Male Biochips Biopsy Neoplasm Grading Tissue Array Analysis Prostatic Neoplasms Pathology Diagnosis, Computer Assisted Deep Learning Staining and Labeling Descriptive Statistics Female Male ab: Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method's effectiveness is verified on a fully annotated dataset. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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