A Multiclassification Framework for Skin Cancer detection by the concatenation of Xception and ResNet101

Clicks: 2
ID: 313077
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #341 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Skin cancer is a deadly type of cancer that is responsible for millions of fatalities annually across the globe. This malignant cancer occurred due to the proliferation of abnormal epidermal cells, which subsequently spread to adjacent tissues and spread to other organs and tissues through the lymph nodes. Changes in lifestyle and sun-seeking behaviors have contributed to the increase in the incidence of skin cancer. It is critical to accurately identify and classify skin cancer to prevent the serious effects that result from delaying detection and treatment. This research paper presents a newly developed deep learning model that makes use of two advanced artificial intelligence techniques, Xception and ResNet101. This method attains an extraordinarily high degree of accuracy by using the special advantages of two strong networks. The Xception-ResNet101 (X_R101) model is capable of differentiating specific categories of skin cancers, such as melanoma (Mel), melanocytic nevus (Mn), basal cell carcinoma (bcc), squamous cell carcinoma (Scc), benign keratosis (Bk), Actinic keratosis (Ak), Dermatofibroma (Df) and Vascular lesion (Vl). The implementation of borderline SMOTE improves performance substantially. A comparison is performed between the proposed methodology and four benchmark classifiers: MobileNetV2 (BM3), DenseNet201 (BM4), InceptionV3 (BM1), and ResNet50 (BM2) and state-of-the-art classifiers. To evaluate performance of the proposed methodology, three publicly available datasets (PH2, DermPK and HAM10000) are utilized. The X_R101 model attains a prediction accuracy of 98.21%. The method's accuracy and effectiveness provide benefits to dermatologists and other healthcare practitioners in terms of timely identification of skin cancer.
Reference Key
imported_1777058878_69ebc43e3b6b5 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tayyaba Anees
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/328
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.