Utilizing a Novel Convolutional Neural Network for Diagnosis and Lesion Delineation in Colorectal Cancer Screening.
Early detection of colorectal cancer is vital for enhancing cure rates and alleviating treatment burdens. Nevertheless, the high demand for screenings coupled with a limited number of endoscopists underscores the necessity for advanced deep learning techniques to improve screening efficiency and acc...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 5; pp. 2841 - 2851 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2025
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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=188953428&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188953428 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Oct2025 vid: 38 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188953428 188953428 189894322 188953428 10.1007/s10278-025-01396-8 188953428 ppf: 2841 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Utilizing a Novel Convolutional Neural Network for Diagnosis and Lesion Delineation in Colorectal Cancer Screening. aug: au: Li, Renbo Cao, Ruofan Zhao, Qi Zhao, Zijian affil: https://ror.org/0207yh398 School of Control Science and Engineering, Shandong University, 250012, Jinan, Shandong, China sug: subj: Colorectal Neoplasms Diagnosis Convolutional Neural Networks Image Interpretation, Computer Assisted Diagnosis, Computer Assisted Early Detection of Cancer Cancer Screening Methods China Funding Source Human Deep Learning Neural Networks (Computer) Multicenter Studies Sensitivity and Specificity Image Processing, Computer Assisted Models, Theoretical Endoscopy Descriptive Statistics Colorectal Neoplasms Classification ab: Early detection of colorectal cancer is vital for enhancing cure rates and alleviating treatment burdens. Nevertheless, the high demand for screenings coupled with a limited number of endoscopists underscores the necessity for advanced deep learning techniques to improve screening efficiency and accuracy. This study presents an innovative convolutional neural network (CNN) model, trained on 8260 images from screenings conducted at four medical institutions. The model incorporates parallel global and local feature extraction branches and a distinctive classification head, facilitating both cancer classification and the creation of heatmaps that outline cancerous lesion regions. Performance evaluations of the CNN model, measured against five leading models using accuracy, precision, recall, and F1 score, revealed its superior efficacy across these metrics. Furthermore, the heatmaps proved effective in aiding the automatic identification of lesion locations. In summary, this CNN model represents a promising advancement in early colorectal cancer screening, delivering precise, swift diagnostic results and robust interpretability through its automatic lesion highlighting capabilities. 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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