{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:53:43Z","timestamp":1769716423575,"version":"3.49.0"},"reference-count":29,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>The most common challenge faced by dermoscopy images is the automatic detection of lesion features. All the existing solutions focus on complex algorithms to provide accurate detections. In this research work, proposed Online Tigerclaw Fuzzy Region Segmentation with Deep Learning Classification model, an intellectual model is proposed that provides discrimination of features with classification even in fine-grained samples. This model works on four different stages, which include the Boosted Anisotropic Diffusion filter with Recursive Pixel Histogram Equalization (BADF-RPHE) in the preprocessing stage. The next step is the proposed Online Tigerclaw Fuzzy Region Segmentation (OTFRS) algorithm for lesion area segmentation of dermoscopic images, which can achieve 98.9% and 97.4% accuracy for benign and malignant lesions, respectively. In the proposed OTFRS, an accuracy improvement of 1.4% is achieved when compared with previous methods. Finally, the increased robustness of lesion classification is achieved using Deep Learning Classification \u2013DenseNet 169 with 500 images. The proposed approach was evaluated with accuracy classifications of 100% and 98.86% for benign and malignant lesions, respectively, and a processing time of less than 18\u200asec. In the proposed DensetNet-169 classification technique, an accuracy improvement of 3% is achieved when compared with other state-of-art methods. A higher range of true positive values is obtained for the Region of Convergence (ROC) curve, which indicates that the proposed work ensures better performance in clinical diagnosis for accurate feature visualization analysis. The methodology has been validated to prove its effectiveness and throw light on the lives of affected patients so they can resume normalcy and live long. The research work was tested in real-time clinical samples, which delivered promising and encouraging results in skin cell detection procedures.<\/jats:p>","DOI":"10.3233\/jifs-233024","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T11:18:26Z","timestamp":1691493506000},"page":"6943-6958","source":"Crossref","is-referenced-by-count":11,"title":["Artificial Intelligence based real-time automatic detection and classification of skin lesion in dermoscopic samples using DenseNet-169 architecture"],"prefix":"10.1177","volume":"45","author":[{"given":"A.","family":"Ashwini","sequence":"first","affiliation":[{"name":"Department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, TamilNadu"}]},{"given":"K.E.","family":"Purushothaman","sequence":"additional","affiliation":[{"name":"Department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, TamilNadu"}]},{"given":"A.","family":"Rosi","sequence":"additional","affiliation":[{"name":"Department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, TamilNadu"}]},{"given":"T.","family":"Vaishnavi","sequence":"additional","affiliation":[{"name":"Department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, TamilNadu"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/JIFS-233024_ref2","doi-asserted-by":"crossref","first-page":"9963","DOI":"10.3233\/JIFS-202566","article-title":"Efficient Unet with depth-aware gated fusion for automatic skin lesion segmentation","volume":"40","author":"Ding","year":"2021","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-233024_ref3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jbi.2018.08.006","article-title":"Skin lesion classification with ensembles of deep convolutional neural networks","volume":"86","author":"Harangi","year":"2018","journal-title":"Journal of Biomedical Informatics"},{"issue":"9","key":"10.3233\/JIFS-233024_ref4","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/10.312091","article-title":"Neural network diagnosis of malignant melanoma from color images","volume":"41","author":"Ercal","year":"1994","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"10.3233\/JIFS-233024_ref5","doi-asserted-by":"crossref","unstructured":"Thomas S.M. , Lefevre J.G. , Baxter G. and Hamilton N.A. , Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer, Medical Image Analysis 68(101915) (2021).","DOI":"10.1016\/j.media.2020.101915"},{"key":"10.3233\/JIFS-233024_ref6","doi-asserted-by":"crossref","unstructured":"Masood A. , Al-Jumaily A.A. and Adnan T. , Development of automated diagnostic system for skin cancer: Performance analysis of neural network learning algorithms for classification,Springer, Artificial Neural Networks and Machine Learning -. 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