Lorenzovici Noémi, Dulf Eva-H, Mocan Teodora, Mocan Lucian
Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului Str. 28, 400014 Cluj-Napoca, Romania.
Physiological Controls Research Center, Óbuda University, H-1034 Budapest, Hungary.
Diagnostics (Basel). 2021 Mar 14;11(3):514. doi: 10.3390/diagnostics11030514.
Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.
结直肠癌是全球第三大常见且第二大致命的肿瘤，每年导致90万人死亡。在本研究中，设计了一种计算机辅助诊断系统，该系统利用一个由数值（血液和尿液分析）和定性数据（患者生活环境、肿瘤位置、T、N、M、杜克分类、相关病理学、技术方法、并发症、事件、超声尺寸以及定位）组成的创新数据集来检测结直肠癌。智能计算机辅助结直肠癌诊断系统是使用不同的机器学习技术设计的，如分类以及浅层和深层神经网络。使用传统机器学习算法解决二元分类问题所获得的最大准确率为77.8%。然而，用深度神经网络解决回归问题在最小化均方误差方面产生了显著更好的性能，达到了0.0000529的值。
