基于机器学习技术的慢性癌疾病的早期检测
Wafa Almukadi1, Sayed Abdel-Khalek2, Adel A Bahaddad3
1Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
PloS one
|July 15, 2025
概括
这项研究引入了一种深度学习技术,用于使用病理图像检测癌. 该KCDC-SODLPI方法达到88.90%的准确性,改善了脏瘤诊断.
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:
- 细胞癌 (RCC) 是一种普遍存在的癌,及时的组织病理图像 (HI) 分析对诊断至关重要.
- 手动HI分析复杂且耗时,需要自动化解决方案.
- 深度学习 (DL) 方法为HI提供先进的模式识别功能,提高诊断速度和准确性.
研究的目的:
- 介绍一项新的技术,癌检测和分类,采用使用病理图像深度学习的蛇优化器 (KCDC-SODLPI),用于分析癌HIV.
- 从病理图像中利用DL进行自动检测和分类癌.
- 提高瘤诊断的准确性和效率.
主要方法:
- KCDC-SODLPI技术使用高斯过 (GF) 进行图像预处理以减少噪声.
- SE-DenseNet模型用于从HI中提取复杂的模式.
- 一个蛇优化器 (SO) 调整SE-DenseNet模型的超参数.
- 双向长期短期记忆 (BiLSTM) 用于最终的检测和分类.
主要成果:
- 该KCDC-SODLPI技术在检测和分类癌方面表现出高于88.90%的精度.
- 该方法有效地提取复杂的模式,并识别病理图像中的异常.
- 实验验证显示,与生物医学图像数据集上的现有模型相比,性能有所改善.
结论:
- KCDC-SODLPI技术为癌诊断提供了一个有前途的自动化解决方案,使用HIs上的DL进行诊断.
- 这种方法克服了手动分析的局限性,提供了更可靠,更有效的诊断过程.
- 这项研究强调了将先进的计算方法与病学图像分析集成到瘤学中的潜力.
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