An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features.
Melanoma is the most lethal of all skin cancers. This necessitates the need for a machine learning-driven skin cancer detection system to help medical professionals with early detection. We propose an integrated multi-modal ensemble framework that combines deep convolution neural representations wit...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1723 - 1739 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
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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=169808829&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808829 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808829 163901653 169808829 169808829 10.1007/s10278-023-00837-6 169808829 ppf: 1723 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features. aug: au: Sharafudeen, Misaj S S, Vinod Chandra affil: Machine Intelligence Research Laboratory, Department of Computer Science, University of Kerala, Trivandrum, India sug: subj: Skin Abnormalities Diagnosis Neural Networks (Computer) Conceptual Framework Melanoma Diagnosis Human Skin Neoplasms Diagnosis Sensitivity and Specificity Descriptive Statistics Funding Source ab: Melanoma is the most lethal of all skin cancers. This necessitates the need for a machine learning-driven skin cancer detection system to help medical professionals with early detection. We propose an integrated multi-modal ensemble framework that combines deep convolution neural representations with extracted lesion characteristics and patient meta-data. This study intends to integrate transfer-learned image features, global and local textural information, and patient data using a custom generator to diagnose skin cancer accurately. The architecture combines multiple models in a weighted ensemble strategy, which was trained and validated on specific and distinct datasets, namely, HAM10000, BCN20000 + MSK, and the ISIC2020 challenge datasets. They were evaluated on the mean values of precision, recall or sensitivity, specificity, and balanced accuracy metrics. Sensitivity and specificity play a major role in diagnostics. The model achieved sensitivities of 94.15%, 86.69%, and 86.48% and specificity of 99.24%, 97.73%, and 98.51% for each dataset, respectively. Additionally, the accuracy on the malignant classes of the three datasets was 94%, 87.33%, and 89%, which is significantly higher than the physician recognition rate. The results demonstrate that our weighted voting integrated ensemble strategy outperforms existing models and could serve as an initial diagnostic tool for skin cancer. 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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