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

ABSTRACT:

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

SUBMITTER: Martinez-Mas J 

PROVIDER: S-EPMC6660116 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature