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

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.

Fetal MRI as Complementary Study of Congenital Cystic Adenomatoid Malformation During Pregnancy: A Single Case Report.

Fetal lung masses are rare findings in prenatal ultrasound scanning in general population, of which congenital cystic adenomatoid malformation is the most commonly diagnosed type. This paper reports a single case of congenital cystic adenomatoid malformation detected at our hospital and the subsequent clinical follow-up using ultrasound scanning and fetal magnetic resonance imaging.