一种基于人工智能的新预测分析技术,用于检测皮肤癌
Prasanalakshmi Balaji1, Bui Thanh Hung1, Prasun Chakrabarti2
1Data Science Laboratory, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Vietnam.
PeerJ. Computer science
|June 22, 2023
概括
早期发现皮肤癌,包括黑色素瘤,对于治疗至关重要. 这项研究引入了基于人工金的随机森林 (AGEbRF) 模型,用于从皮肤镜图像中准确地细分皮肤癌.
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:
- 皮肤癌是全球主要的死亡原因,早期检测对黑色素瘤治愈能力至关重要.
- 全球超过75%的死亡与皮肤癌有关,这凸显了改善诊断工具的必要性.
研究的目的:
- 开发和评估一种基于人工金的新型随机森林 (AGEbRF) 模型,用于早期皮肤癌细胞预测.
- 在皮肤镜图像中准确识别和细分癌症区域.
主要方法:
- 使用皮肤镜像作为训练AGEbRF模型的数据集.
- 使用AGEbRF算法处理图像数据以检测和细分皮肤癌.
- 使用Python程序模拟了该方法,并将性能与现有研究进行了比较.
主要成果:
- 与其他模型相比,拟议的AGEbRF模型在通过图像细分预测皮肤癌方面表现出卓越的准确性.
- 该模型在皮肤镜图像中有效地识别和细分受影响区域.
结论:
- 新的AGEbRF模型显示了改善早期皮肤癌检测和细分的巨大潜力.
- 这种人工智能驱动的方法为皮肤病诊断提供了有希望的进步,以获得更好的患者结果.
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