胃肠道 (GI) 癌症疾病的高级细分使用新型U-MaskNet模型
Aditya Pal1, Hari Mohan Rai2, Mohamed Ben Haj Frej3
1Department of Information Technology, Dronacharya Group of Institutions, Greater Noida 201306, India.
Life (Basel, Switzerland)
|November 27, 2024
概括
一个新的混合U-Net和Mask R-CNN模型,U-MaskNet,增强了胃肠道癌症的检测. 这种人工智能方法通过提供优越的肠道疾病细分和分类来提高早期诊断准确度和患者护理.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 胃肠道 (GI) 癌症的早期诊断对于患者的治疗结果至关重要.
- 目前的诊断工具在早期检测的效率和准确性方面面临挑战.
- 准确地分类和细分GI疾病对于及时干预至关重要.
研究的目的:
- 开发一种先进的方法来分类和细分各种胃肠道癌症疾病.
- 引入一种新的混合细分模型,U-MaskNet,以改善胃肠道癌症诊断.
- 解决用于早期检测肠道癌症的现有诊断工具的局限性.
主要方法:
- 提出了一种混合细分模型,U-MaskNet,将U-Net用于像素分类和Mask R-CNN用于例如细分.
- 利用Kvasir数据集,包括8000个肠道癌症内镜图像,用于模型验证.
- 我们将U-MaskNet的性能与DeepLabv3+,FCN,DeepMask,LeNet-5,AlexNet,VGG-16,ResNet-50和Inception Network等既有模型进行了比较.
主要成果:
- 与DeepLabv3 +,FCN和DeepMask相比,U-MaskNet表现出优越的细分性能.
- 与包括ResNet-50和Inception Network在内的最先进模型相比,实现了更好的分类性能.
- 定量分析显示,U-MaskNet实现了98.85%的精度,98.49%的回忆,98.68%的F1得分,94.35%的子系数,89.31%的IOU.
结论:
- 开发的U-MaskNet模型显著提高了检测和细分肠道癌症的准确性和可靠性.
- 整合U-Net和Mask R-CNN模型为改善胃肠道癌症诊断提供了一个有希望的解决方案.
- 这项研究为通过先进的医学图像细分提高临床诊断过程和改善患者护理铺平了道路.
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