Deep Learning–Based Skin Lesion Multi-class Classification with Global Average Pooling Improvement.
Cancerous skin lesions are one of the deadliest diseases that have the ability in spreading across other body parts and organs. Conventionally, visual inspection and biopsy methods are widely used to detect skin cancers. However, these methods have some drawbacks, and the prediction is not highly ac...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2227 - 2249 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
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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=171950867&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950867 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950867 164736993 171950867 171950867 10.1007/s10278-023-00862-5 171950867 ppf: 2227 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning–Based Skin Lesion Multi-class Classification with Global Average Pooling Improvement. aug: au: Raghavendra, Paravatham V. S. P. Charitha, C. Begum, K. Ghousiya Prasath, V. B. S. affil: School of Mechanical Engineering, SASTRA Deemed to be University, 613401, Thanjavur, India sug: subj: Skin Neoplasms Diagnosis Skin Neoplasms Classification Deep Learning Utilization Neural Networks (Computer) Human Female Male Diagnosis, Computer Assisted Validity Graphical User Interface Prediction Models Minimum Data Set Data Analysis Image Processing, Computer Assisted ROC Curve Descriptive Statistics Female Male ab: Cancerous skin lesions are one of the deadliest diseases that have the ability in spreading across other body parts and organs. Conventionally, visual inspection and biopsy methods are widely used to detect skin cancers. However, these methods have some drawbacks, and the prediction is not highly accurate. This is where a dependable automatic recognition system for skin cancers comes into play. With the extensive usage of deep learning in various aspects of medical health, a novel computer-aided dermatologist tool has been suggested for the accurate identification and classification of skin lesions by deploying a novel deep convolutional neural network (DCNN) model that incorporates global average pooling along with preprocessing to discern the skin lesions. The proposed model is trained and tested on the HAM10000 dataset, which contains seven different classes of skin lesions as target classes. The black hat filtering technique has been applied to remove artifacts in the preprocessing stage along with the resampling techniques to balance the data. The performance of the proposed model is evaluated by comparing it with some of the transfer learning models such as ResNet50, VGG-16, MobileNetV2, and DenseNet121. The proposed model provides an accuracy of 97.20%, which is the highest among the previous state-of-art models for multi-class skin lesion classification. The efficacy of the proposed model is also validated by visualizing the results obtained using a graphical user interface (GUI). pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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