Title: Machine learning based ovarian detection in ultrasound images

Authors: V. Kiruthika; S. Sathiya; M.M. Ramya

Addresses: Department of Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Chennai-603103, India ' Department of Obstetrics and Gynaecology, Chettinad Hospital and Research Institute, Chennai-603103, India ' Centre for Automation and Robotics, Hindustan Institute of Technology and Science, Chennai-603103, India

Abstract: Computer aided ovarian detection and ovarian classification is important in infertility treatment in women. In the proposed methodology, an intelligent automatic detection and ovarian classification with grading based on integration of intensity and texture features using artificial neural network is developed. Three texture features such as autocorrelation, sum average and sum variance obtained from grey-level co-occurrence matrix (GLCM) and intensity obtained using K-means clustering were fed as input to the multilayer feedforward backpropagation network for ovarian detection. Ovarian morphology was used for classification and grading of ovary. This novel technique helps the physician to grade the follicle/cyst. Performance metrics like sensitivity, specificity, accuracy, precision, F-measure, Mathew's correlation coefficient and receiver operating characteristic curve were used to prove the effectiveness of the proposed machine learning based ovarian detection (MLOD). The MLOD classifier yielded an average detection accuracy of 96% which is an increase of 2% as compared to the combined texture and intensity based ovarian classification (TIOC) algorithm.

Keywords: colour space transform; discrete wavelet transform; DWT; K-means clustering; texture features; intensity based segmentation; machine learning; intelligent classifier; artificial neural network; ANN; ovarian detection; ovarian classification.

DOI: 10.1504/IJAMECHS.2020.111306

International Journal of Advanced Mechatronic Systems, 2020 Vol.8 No.2/3, pp.75 - 85

Received: 14 Oct 2019
Accepted: 24 Apr 2020
Published online: 19 Nov 2020 *

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