ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images.
Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 2; pp. 526 - 536 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2023
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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=162679416&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162679416 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2023 vid: 36 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162679416 160256930 162679416 162679416 10.1007/s10278-022-00740-6 162679416 ppf: 526 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images. aug: au: Lama, Norsang Kasmi, Reda Hagerty, Jason R. Stanley, R. Joe Young, Reagan Miinch, Jessica Nepal, Januka Nambisan, Anand Stoecker, William V. affil: Missouri University of Science & Technology, 65409, Rolla, MO, USA sug: subj: Image Processing, Computer Assisted Methods Dermoscopy Methods Deep Learning Methods Skin Diseases Hair Pathology Melanoma Skin Neoplasms Pathology Human Neural Networks (Computer) Methods Hair Analysis Hair Removal Sensitivity and Specificity Descriptive Statistics Comparative Studies Early Diagnosis Needs Assessment Health Promotion Health Information Funding Source ab: Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images. Our proposed ChimeraNet is an encoder-decoder architecture that employs pretrained EfficientNet in the encoder and squeeze-and-excitation residual (SERes) structures in the decoder. We applied this approach at multiple image sizes and evaluated it using the publicly available HAM10000 (ISIC2018 Task 3) skin lesion dataset. Our test results show that the largest image size (448 × 448) gave the highest accuracy of 98.23 and Jaccard index of 0.65 on the HAM10000 (ISIC 2018 Task 3) skin lesion dataset, exhibiting better performance than for two well-known deep learning approaches, U-Net and ResUNet-a. We found the Dice loss function to give the best results for all measures. Further evaluated on 25 additional test images, the technique yields state-of-the-art accuracy compared to 8 previously reported classical techniques. We conclude that the proposed ChimeraNet architecture may enable improved detection of fine image structures. Further application of DL techniques to detect dermoscopy structures is warranted. 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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