Multi-Skin disease classification using hybrid deep learning model.

IF 1.4 4区医学 Q4 ENGINEERING, BIOMEDICAL

Technology and Health Care Pub Date : 2025-02-02 DOI:10.1177/09287329241312628

K Jeyageetha, K Vijayalakshmi, S Suresh, A Bhuvanesh

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引用次数: 0

Abstract

Among the many cancers that people face today, skin cancer is among the deadliest and most dangerous. As a result, improving patients' chances of survival requires skin cancer to be identified and classified early. Therefore, it is critical to assist radiologists in detecting skin cancer through the development of Computer Aided Diagnosis (CAD) techniques. The diagnostic procedure currently makes heavy use of Deep Learning (DL) techniques for disease identification. In addition, skin lesion extraction and improved classification performance are achieved through Region Growing (RG) based segmentation. At the outset of this study, noise is reduced using an Adaptive Wiener Filter (AWF), and hair is removed using a Maximum Gradient Intensity (MGI). Then, the best RG, which is the result of integrating RG with the Modified Honey Badger Optimiser (MHBO), does the segmentation. Finally, several forms of skin cancer are classified using the DL model MobileSkinNetV2. The experiments were conducted on the ISIC dataset and the results show that the accuracy and precision were improved to 99.01% and 98.6%, respectively. In comparison to existing models, the experimental results show that the proposed model performs competitively, which is great news for dermatologists treating cancer.

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基于混合深度学习模型的多种皮肤病分类。

在当今人们面临的许多癌症中，皮肤癌是最致命和最危险的癌症之一。因此，要提高患者的生存机会，就需要及早发现和分类皮肤癌。因此，通过计算机辅助诊断（CAD）技术的发展来协助放射科医生检测皮肤癌是至关重要的。诊断程序目前大量使用深度学习（DL）技术来识别疾病。此外，通过基于区域生长（RG）的分割，实现了皮肤病灶的提取和分类性能的提高。在本研究开始时，使用自适应维纳滤波器（AWF）减少噪声，并使用最大梯度强度（MGI）去除毛发。然后，将RG与改进的蜜獾优化器（MHBO）相结合的最佳RG进行分割。最后，使用DL模型MobileSkinNetV2对几种类型的皮肤癌进行分类。在ISIC数据集上进行了实验，结果表明，该方法的准确度和精密度分别提高到99.01%和98.6%。与现有模型相比，实验结果表明，所提出的模型具有竞争力，这对皮肤科医生治疗癌症来说是一个好消息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Technology and Health Care HEALTH CARE SCIENCES & SERVICES-ENGINEERING, BIOMEDICAL

CiteScore

2.10

自引率

6.20%

发文量

282

审稿时长

>12 weeks

期刊介绍： Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered: 1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables. 2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words. Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics. 4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors. 5.Letters to the Editors: Discussions or short statements (not indexed).