Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images

Martínez-Más, José and Bueno-Crespo, Andrés and Khazendar, Shan and Remezal, Manuel and Martínez-Cendán, Juan-Pedro and Jassim, Sabah A. and Du, Hongbo and Al-Assam, Hisham and Bourne, Tom and Timmerman, Dirk (2019) Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images. PloS ONE, 14 (7). pp. 1-14. ISSN 1932-6203

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Introduction: Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images. Methods: We have used a real patient database whose input features have been extracted from 348 images, from the IOTA tumor images database, holding together with the class labels of the images. For each patient case and ultrasound image, its input features have been previously extracted using Fourier descriptors computed on the Region Of Interest (ROI). Then, four ML techniques are considered for performing the classification stage: K-Nearest Neighbors (KNN), Linear Discriminant (LD), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Results: According to our obtained results, the KNN classifier provides inaccurate predictions (less than 60% of accuracy) independently of the size of the local approximation, whereas the classifiers based on LD, SVM and ELM are robust in this biomedical classification (more than 85% of accuracy). Conclusions: ML methods can be efficiently used for developing the classification stage in computer-aided diagnosis systems of ovarian tumor from ultrasound images. These approaches are able to provide automatic classification with a high rate of accuracy. Future work should aim at enhancing the classifier design using ensemble techniques. Another ongoing work is to exploit different kind of features extracted from ultrasound images.

Item Type: Article
Uncontrolled Keywords: Machine learning, Ultrasound images, Ovarian tumours
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Hongbo Du
Date Deposited: 30 Jul 2019 15:07
Last Modified: 31 Jul 2019 08:30
URI: http://bear.buckingham.ac.uk/id/eprint/391

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