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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Updated: Sep 18, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

使用和棕熊优化的皮肤癌检测启用了转移学习优化.

Malathy Manickavasagam¹, Vaddadi Vasudha Rani², Uttam Kumar Giri³

¹Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai 600062, India.

Computational biology and chemistry

|June 21, 2025

概括

此摘要是机器生成的。

这项研究引入了一种新的卷积神经网络转移学习方法,增强了和棕熊优化 (CNN-TL_Hr-BOA),用于准确检测皮肤癌. 该方法通过有效分析皮肤病变图像,显著改善了早期诊断.

关键词:

梅达夫过器可以过.位置和上下文信息融合注意力网络注意力皮肤癌检测皮肤癌检测皮肤图像皮肤图像皮肤图像转移学习转移学习

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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Last Updated: Sep 18, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Published on: May 5, 2011

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

科学领域:

皮肤病学皮肤病学
医疗成像医学成像
人工智能的人工智能

背景情况:

早期发现皮肤癌对于成功治疗至关重要,但由于良性病变和恶性病变之间的视觉相似性,因此具有挑战性.
现有的诊断方法在识别皮肤病变的微妙差异时,往往在准确性和稳定性方面扎.

研究的目的:

开发一个先进的深度学习框架,CNN-TL_Hr-BOA,以提高皮肤癌早期检测的准确性和稳定性.
通过复杂的图像处理和优化模型培训,改善恶性皮肤病变的识别.

主要方法:

图像的预处理包括使用Medav波器去噪声和使用PCF-Net.Net进行细分.
超像素混合增强多元化训练数据,然后使用RDWT进行特征提取.
一个使用DenseNet权重的CNN模型使用波分析和棕熊优化 (Hr-BOA) 进行了优化,用于超参数调整.

主要成果:

在SIIM-ISIC黑色素瘤分类数据集中,CNN-TL_Hr-BOA模型取得了高性能.
关键指标包括91.754%的准确率,93.755%的真正比率 (TPR) 和89.766%的真负比率 (TNR).

结论:

拟议的CNN-TL_Hr-BOA框架在准确检测早期皮肤癌方面表现出显著的有效性.
这种方法为改善皮肤癌识别医学成像诊断准确度提供了强大的解决方案.