Kruczkowski Michał, Drabik-Kruczkowska Anna, Marciniak Anna, Tarczewska Martyna, Kosowska Monika, Szczerska Małgorzata
Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Al. prof. S. Kaliskiego 7, 85-796, Bydgoszcz, Poland.
Department of Obstetrics, Gynaecology and Oncology, Faculty of Medicine, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-094, Bydgoszcz, Poland.
Sci Rep. 2022 Mar 8;12(1):3762. doi: 10.1038/s41598-022-07723-1.
Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors-to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing an optical sensor and a prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing four algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes, Convolutional Neural Networks) and assessment of their performance for classification of tissue as healthy or sick. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.
宫颈癌是最常见的癌症之一，其早期诊断至关重要。不幸的是，许多诊断基于医生的主观意见——迄今为止，尚无具有校准标准的通用测量方法。该问题可以通过将光电传感器和机器学习算法融合的测量系统来解决，以便在宫颈癌早期诊断阶段为医生提供可靠的辅助。我们展示了利用光学传感器和预测算法对宫颈癌评估的初步研究。由于每种物质都具有折射率特征，测量其值并检测变化可提供有关组织状态的信息。光学测量为训练和验证分析软件提供了数据集。我们展示了数据预处理、使用四种算法（随机森林、极端梯度提升、朴素贝叶斯、卷积神经网络）的机器学习结果以及对其将组织分类为健康或患病的性能评估。我们的解决方案允许快速样本测量和结果自动分类，构成了医生潜在的支持工具。
