Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning
Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning
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DOI：
10.26599/bdma.2020.9020023
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发表时间：
2021-03-01
影响因子：
13.6
通讯作者：
Agoujil, Said
中科院分区：
文献类型：
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作者：
Alaoui, El Arbi Abdellaoui;Koumetio Tekouabou, Stephane Cedric;Agoujil, Said
软组织肿瘤(STT)是一种肉瘤，发现于连接、支持和包围身体结构的组织中。由于它们在体内的频率很低，而且差异很大，通过磁共振成像(MRI)观察时，它们似乎是不同的。容易与乳腺纤维腺瘤、淋巴结病、结节性甲状腺肿等疾病相混淆，对患者的医疗过程造成相当大的不利影响。研究人员已经提出了几种机器学习模型来对肿瘤进行分类，但没有一种模型能够充分解决这个误诊问题。此外，提出评估这类肿瘤的模型的类似研究大多没有考虑数据的异质性和大小。因此，我们提出了一种基于机器学习的方法，它结合了用于特征变换的数据预处理技术、消除不稳定偏差的重采样技术以及基于支持向量机和决策树(DT)算法的分类器测试。在印度尼西亚日惹Nur Hidayah医院收集的数据集上进行的测试表明，与之前的研究相比，有了很大的改善。这些结果证实了机器学习方法可以为加强STT诊断的自动决策过程提供高效和有效的工具。
Soft Tissue Tumors (STT) are a form of sarcoma found in tissues that connect, support, and surround body structures. Because of their shallow frequency in the body and their great diversity, they appear to be heterogeneous when observed through Magnetic Resonance Imaging (MRI). They are easily confused with other diseases such as fibroadenoma mammae, lymphadenopathy, and struma nodosa, and these diagnostic errors have a considerable detrimental effect on the medical treatment process of patients. Researchers have proposed several machine learning models to classify tumors, but none have adequately addressed this misdiagnosis problem. Also, similar studies that have proposed models for evaluation of such tumors mostly do not consider the heterogeneity and the size of the data. Therefore, we propose a machine learning-based approach which combines a new technique of preprocessing the data for features transformation, resampling techniques to eliminate the bias and the deviation of instability and performing classifier tests based on the Support Vector Machine (SVM) and Decision Tree (DT) algorithms. The tests carried out on dataset collected in Nur Hidayah Hospital of Yogyakarta in Indonesia show a great improvement compared to previous studies. These results confirm that machine learning methods could provide efficient and effective tools to reinforce the automatic decision-making processes of STT diagnostics.