Psoriatic Disease Type Prediction and Analysis Using Deep Feature Learning Model

IF 1.9 4区计算机科学 Q3 TELECOMMUNICATIONS

Wireless Personal Communications Pub Date : 2024-09-09 DOI:10.1007/s11277-024-11537-x

S. V. Anandhi, G. Wiselin Jiji

引用次数: 0

Abstract

This work focus on the classification of skin surface images to identify the psoriatic type. To learn and analysis the deep insight of the psoriatic images a custom Convolutional Neural Network (CNN) developed as a prediction model. Before get into the learning process, the input images are involved with segmentation operation. For this purpose, color and texture feature-based segmentation is utilized. The custom architecture of the CNN is formulated to deliver the superior psoriatic disease type prediction result. The model has experimented with native collected data set and performance measures are analyzed. The results shows that the proposed method has high contribute in terms of psoriasis classification and severity grading with an accuracy of 98.94%.

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利用深度特征学习模型预测和分析银屑病类型

这项工作的重点是对皮肤表面图像进行分类，以识别银屑病类型。为了学习和分析对银屑病图像的深入洞察，开发了一个定制的卷积神经网络（CNN）作为预测模型。在进入学习过程之前，输入图像需要进行分割操作。为此，利用了基于颜色和纹理特征的分割。CNN 的定制架构旨在提供卓越的银屑病类型预测结果。该模型利用本地收集的数据集进行了实验，并对性能指标进行了分析。结果表明，所提出的方法在牛皮癣分类和严重程度分级方面有很高的贡献，准确率高达 98.94%。

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

Wireless Personal Communications 工程技术-电信学

CiteScore

5.80

自引率

9.10%

发文量

663

审稿时长

6.8 months

期刊介绍： The Journal on Mobile Communication and Computing ... Publishes tutorial, survey, and original research papers addressing mobile communications and computing; Investigates theoretical, engineering, and experimental aspects of radio communications, voice, data, images, and multimedia; Explores propagation, system models, speech and image coding, multiple access techniques, protocols, performance evaluation, radio local area networks, and networking and architectures, etc.; 98% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again. Wireless Personal Communications is an archival, peer reviewed, scientific and technical journal addressing mobile communications and computing. It investigates theoretical, engineering, and experimental aspects of radio communications, voice, data, images, and multimedia. A partial list of topics included in the journal is: propagation, system models, speech and image coding, multiple access techniques, protocols performance evaluation, radio local area networks, and networking and architectures. In addition to the above mentioned areas, the journal also accepts papers that deal with interdisciplinary aspects of wireless communications along with: big data and analytics, business and economy, society, and the environment. The journal features five principal types of papers: full technical papers, short papers, technical aspects of policy and standardization, letters offering new research thoughts and experimental ideas, and invited papers on important and emerging topics authored by renowned experts.