Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.
Skin cancer is one of the top three hazardous cancer types, and it is caused by the abnormal proliferation of tumor cells. Diagnosing skin cancer accurately and early is crucial for saving patients' lives. However, it is a challenging task due to various significant issues, including lesion variatio...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1755 - 1776 |
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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=185280528&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280528 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: 185280528 185280528 185280528 10.1007/s10278-024-01290-9 185280528 ppf: 1755 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach. aug: au: Paraddy, Sudha Virupakshappa affil: https://ror.org/00ha14p11 Computer Science & Engineering, PDA College of Engineering, Kalaburagi, India sug: subj: Skin Neoplasms Diagnosis Deep Learning Methods Skin Neoplasms Classification Convolutional Neural Networks Utilization Sensitivity and Specificity Human Image Processing, Computer Assisted Data Management Data Mining Conceptual Framework Artifacts Minimum Data Set Workflow ab: Skin cancer is one of the top three hazardous cancer types, and it is caused by the abnormal proliferation of tumor cells. Diagnosing skin cancer accurately and early is crucial for saving patients' lives. However, it is a challenging task due to various significant issues, including lesion variations in texture, shape, color, and size; artifacts (hairs); uneven lesion boundaries; and poor contrast. To solve these issues, this research proposes a novel Convolutional Swin Transformer (CSwinformer) method for segmenting and classifying skin lesions accurately. The framework involves phases such as data preprocessing, segmentation, and classification. In the first phase, Gaussian filtering, Z-score normalization, and augmentation processes are executed to remove unnecessary noise, re-organize the data, and increase data diversity. In the phase of segmentation, we design a new model "Swinformer-Net" integrating Swin Transformer and U-Net frameworks, to accurately define a region of interest. At the final phase of classification, the segmented outcome is input into the newly proposed module "Multi-Scale Dilated Convolutional Neural Network meets Transformer (MD-CNNFormer)," where the data samples are classified into respective classes. We use four benchmark datasets—HAM10000, ISBI 2016, PH2, and Skin Cancer ISIC for evaluation. The results demonstrated the designed framework's better efficiency against the traditional approaches. The proposed method provided classification accuracy of 98.72%, pixel accuracy of 98.06%, and dice coefficient of 97.67%, respectively. The proposed method offered a promising solution in skin lesion segmentation and classification, supporting clinicians to accurately diagnose 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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