用于 iOS 皮肤病变分类的机器学习方法：利用 Create ML 实现并优化卷积传递学习模型

Q2 Computer Science

International Journal of Computers and Applications Pub Date : 2024-07-24 DOI:10.1080/1206212x.2024.2380664

Aron Benedek Szabo, Jozsef Katona

引用次数: 0

摘要

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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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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来源期刊

International Journal of Computers and Applications Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

4.70

自引率

0.00%

发文量

期刊介绍： The International Journal of Computers and Applications (IJCA) is a unique platform for publishing novel ideas, research outcomes and fundamental advances in all aspects of Computer Science, Computer Engineering, and Computer Applications. This is a peer-reviewed international journal with a vision to provide the academic and industrial community a platform for presenting original research ideas and applications. IJCA welcomes four special types of papers in addition to the regular research papers within its scope: (a) Papers for which all results could be easily reproducible. For such papers, the authors will be asked to upload "instructions for reproduction'''', possibly with the source codes or stable URLs (from where the codes could be downloaded). (b) Papers with negative results. For such papers, the experimental setting and negative results must be presented in detail. Also, why the negative results are important for the research community must be explained clearly. The rationale behind this kind of paper is that this would help researchers choose the correct approaches to solve problems and avoid the (already worked out) failed approaches. (c) Detailed report, case study and literature review articles about innovative software / hardware, new technology, high impact computer applications and future development with sufficient background and subject coverage. (d) Special issue papers focussing on a particular theme with significant importance or papers selected from a relevant conference with sufficient improvement and new material to differentiate from the papers published in a conference proceedings.