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Updated: Jun 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

使用定制深度可学习层与可解释的人工智能 (XAI) 来检测蓝色面和病变分类.

M A Rasel¹, Sameem Abdul Kareem¹, Zhenli Kwan²

¹Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.

Computers in biology and medicine

|June 21, 2024

概括

这项研究引入了一种新的深度卷积神经网络 (DCNN),用于检测皮肤病变中的蓝白面 (BWV),这是黑色素瘤的关键指标. 该DCNN显著提高了早期黑色素瘤诊断的准确性.

关键词:

一个蓝色的面.DCNN DCN 在线网络皮肤镜像图像皮肤镜像图像在 LIME 时代,黑色素瘤是一种黑色素瘤.

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科学领域:

皮肤病学和人工智能研究
医学成像分析医学成像分析
在瘤学瘤学.

背景情况:

黑色素瘤是一种致命的皮肤癌,在全球造成数千人死亡.
蓝白面 (BWV) 是黑色素瘤的一个关键诊断特征.
在皮肤学图像中自动检测BWV存在有限的研究.

研究的目的:

开发和评估一个深度卷积神经网络 (DCNN),用于在皮肤病变中准确检测BWV.
通过加强BWV识别来改善早期黑色素瘤诊断.
为了利用可解释的人工智能 (XAI) 来实现模型的解释性.

主要方法:

一个没有注释的数据集被转换成一个注释的,使用一种新的成像算法与颜色值技术.
一个DCNN是用定制层设计的,并对单个和组合的皮肤镜数据集进行训练.
应用了一个可解释的人工智能 (XAI) 算法来解释DCNN的决策过程.

主要成果:

拟议的DCNN实现了高的测试准确率: 85.71% (PH2),95.00% (ISIC),95.05% (组合) 和90.00% (Derm7pt).
在各种数据集中,DCNN的性能优于传统的BWV检测模型.
整合XAI为DCNN的BWV检测机制提供了洞察力.

结论:

开发的DCNN与XAI相结合,显著提高了皮肤病变中的BWV检测.
这种方法为改善早期黑色素瘤诊断提供了一个强大的工具.
该研究强调了AI在皮肤学图像分析中用于癌症检测的潜力.