Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification.
Colorectal cancer (CRC) remains one of the most common and lethal malignancies worldwide. The current gold standard for CRC diagnosis relies on histopathological analysis, a time-consuming process subject to inter-observer variability and dependent on expert experience. While convolutional neural ne...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 2; pp. 1558 - 1573 |
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| Autores principales: | , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2026
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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=193167868&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193167868 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: Apr2026 vid: 39 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 193167868 189894285 193167868 193167868 10.1007/s10278-025-01633-0 193167868 ppf: 1558 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification. aug: au: Rodriges Moreira, Larissa Ferreira Backes, André Ricardo affil: https://ror.org/0409dgb37 Institute of Exacts and Technological Sciences, Federal University of Viçosa, Viçosa, Brazil sug: subj: Colorectal Neoplasms Classification Image Processing, Computer Assisted Deep Learning Convolutional Neural Networks Human Germany Funding Source Sensitivity and Specificity Descriptive Statistics Automation Data Analysis Software Analysis of Variance Paired T-Tests Colorectal Neoplasms Diagnosis ab: Colorectal cancer (CRC) remains one of the most common and lethal malignancies worldwide. The current gold standard for CRC diagnosis relies on histopathological analysis, a time-consuming process subject to inter-observer variability and dependent on expert experience. While convolutional neural networks (CNNs) have achieved remarkable success in medical image analysis, they often require large annotated datasets and lack interpretability. Traditional handcrafted texture descriptors, on the other hand, provide domain-specific insights but may fall short in capturing complex patterns. To address these limitations, we propose a novel ensemble approach that integrates handcrafted texture descriptors with deep learning-based features extracted from CNNs. Our method leverages the complementary strengths of both feature types, resulting in a more robust and discriminative feature space. Experimental evaluations demonstrate that our ensemble approach outperforms state-of-the-art methods across various metrics, achieving an accuracy of 99.20% by combining color textures with deep learning features. This study underscores the potential of integrating traditional and modern techniques to advance medical image analysis, presenting a significant step forward in automated CRC classification and fostering advancements in medical computing and image processing. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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