MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features.
The increasing prevalence of skin diseases necessitates accurate and efficient diagnostic tools. This research introduces a novel skin disease classification model leveraging advanced deep learning techniques. The proposed architecture combines the MobileNet-V2 backbone, Squeeze-and-Excitation (SE)...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1734 - 1755 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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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=185280515&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280515 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: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280515 185280515 185280515 10.1007/s10278-024-01271-y 185280515 ppf: 1734 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features. aug: au: Nirupama Virupakshappa affil: Department of Artificial Intelligence and Machine Learning, Sharnbasva University Kalaburagi, Kalaburagi, Karnataka, India sug: subj: Skin Diseases Diagnosis Skin Diseases Classification Image Processing, Computer Assisted Methods Mobile Applications Sensitivity and Specificity Human Comparative Studies Validation Studies Dermoscopy Medicine, Traditional Machine Learning Descriptive Statistics Dermatologists Dermatology Decision Making Attention ROC Curve Confidence Intervals Paired T-Tests Friedman Test Ablation Techniques ab: The increasing prevalence of skin diseases necessitates accurate and efficient diagnostic tools. This research introduces a novel skin disease classification model leveraging advanced deep learning techniques. The proposed architecture combines the MobileNet-V2 backbone, Squeeze-and-Excitation (SE) blocks, Atrous Spatial Pyramid Pooling (ASPP), and a Channel Attention Mechanism. The model was trained on four diverse datasets such as PH2 dataset, Skin Cancer MNIST: HAM10000 dataset, DermNet. dataset, and Skin Cancer ISIC dataset. Data preprocessing techniques, including image resizing, and normalization, played a crucial role in optimizing model performance. In this paper, the MobileNet-V2 backbone is implemented to extract hierarchical features from the preprocessed dermoscopic images. The multi-scale contextual information is fused by the ASPP model for generating a feature map. The attention mechanisms contributed significantly, enhancing the extraction ability of inter-channel relationships and multi-scale contextual information for enhancing the discriminative power of the features. Finally, the output feature map is converted into probability distribution through the softmax function. The proposed model outperformed several baseline models, including traditional machine learning approaches, emphasizing its superiority in skin disease classification with 98.6% overall accuracy. Its competitive performance with state-of-the-art methods positions it as a valuable tool for assisting dermatologists in early classification. The study also identified limitations and suggested avenues for future research, emphasizing the model's potential for practical implementation in the field of dermatology. 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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