Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.
In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explainability. To dermatologists, the presence of telan...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 3; pp. 1137 - 1151 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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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=178678179&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678179 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2024 vid: 37 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678179 178678179 178678179 10.1007/s10278-024-00969-3 178678179 ppf: 1137 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features. aug: au: Maurya, Akanksha Stanley, R. Joe Aradhyula, Hemanth Y. Lama, Norsang Nambisan, Anand K. Patel, Gehana Saeed, Daniyal Swinfard, Samantha Smith, Colin Jagannathan, Sadhika Hagerty, Jason R. Stoecker, William V. affil: https://ror.org/00scwqd12 Missouri University of Science &Technology, 65209, Rolla, MO, USA sug: subj: Carcinoma, Basal Cell Diagnosis Carcinoma, Basal Cell Pathology Deep Learning Telangiectasis Pathology Human Sensitivity and Specificity Dermoscopy Image Interpretation, Computer Assisted Skin Neoplasms Pathology Carcinoma, Basal Cell Classification Descriptive Statistics ab: In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explainability. To dermatologists, the presence of telangiectasia, or narrow blood vessels that typically appear serpiginous or arborizing, is a critical indicator of basal cell carcinoma (BCC). Exploiting the feature information present in telangiectasia through a combination of DL-based techniques could create a pathway for both, improving DL results as well as aiding dermatologists in BCC diagnosis. This study demonstrates a novel "fusion" technique for BCC vs non-BCC classification using ensemble learning on a combination of (a) handcrafted features from semantically segmented telangiectasia (U-Net-based) and (b) deep learning features generated from whole lesion images (EfficientNet-B5-based). This fusion method achieves a binary classification accuracy of 97.2%, with a 1.3% improvement over the corresponding DL-only model, on a holdout test set of 395 images. An increase of 3.7% in sensitivity, 1.5% in specificity, and 1.5% in precision along with an AUC of 0.99 was also achieved. Metric improvements were demonstrated in three stages: (1) the addition of handcrafted telangiectasia features to deep learning features, (2) including areas near telangiectasia (surround areas), (3) discarding the noisy lower-importance features through feature importance. Another novel approach to feature finding with weak annotations through the examination of the surrounding areas of telangiectasia is offered in this study. The experimental results show state-of-the-art accuracy and precision in the diagnosis of BCC, compared to three benchmark techniques. Further exploration of deep learning techniques for individual dermoscopy feature detection is warranted. 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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