基于CNN的深度学习架构的比较分析,用于早期诊断骨癌,使用CT图像
Kanimozhi Sampath1, Sivakumar Rajagopal2, Ananthakrishna Chintanpalli3
1Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India.
Scientific reports
|January 25, 2024
概括
早期发现骨癌可以提高生存率. 这项研究使用图像处理和深度学习 (卷积神经网络) 准确分类癌症骨图像,帮助早期诊断.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 骨癌是一种罕见的疾病,涉及不受控制的骨细胞生长,可能扩散到身体的其他部位.
- 早期检测至关重要,因为骨癌患者的生存率约为40%,早期诊断显著改善了结果.
- 及时识别骨瘤可以降低死亡率,并促进及时治疗.
研究的目的:
- 采用图像处理和深度学习,特别是卷积神经网络 (CNN),用于分类正常与癌症骨图像.
- 通过先进的计算技术,提高骨癌检测的准确性.
主要方法:
- 医疗图像处理技术,包括预处理的中位过,细分的K-means聚类和Cannie边缘检测,应用于计算机断层扫描 (CT) 图像.
- 这些技术被用来识别特定类型的骨癌中的癌症区域:骨骨髓骨肉瘤,骨髓骨质瘤和骨髓骨质瘤.
- 使用已建立的CNN模型进行了正常和癌症骨图像的分类.
主要成果:
- 亚历克斯网络模型在分类骨图像方面表现出卓越的性能.
- 亚历克斯网络模型实现了98%的训练精度,98%的验证精度和100%的测试精度.
结论:
- 图像处理和深度学习的整合,特别是像AlexNet这样的CNN,为从CT图像中分类骨癌提供了一种高度有效的方法.
- 这种方法显示了精确和早期检测骨癌的巨大潜力,有助于改善患者的生存率.
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