Hybrid Textural Feature Descriptors for Ovarian Cyst Classification Using Machine Learning

A computer aided diagnosis system for classification of ovarian cyst is a crucial step towards treatment of women suffering from cysts in ovaries. Ultrasound or sonography is an efficacious technique that assists healthcare providers in diagnosis and therapeutics for cysts in ovaries. In this research, an automated ovarian cyst diagnosis system based on textural features from ultrasound images is designed. Local binary pattern and fractal dimension textual features are combined to deduce features related to texture from ultrasound images and these textural features are given as input to machine learning classifiers like, decision tree, logistic regression and support vector machine. This novel method assists healthcare providers to distinguish cystic ovaries from normal ovaries in ultrasound images. Evaluation metrics namely, precision, accuracy, recall, F1-score, specificity and receiver operating characteristics – area under curve are used to validate the efficacy of the classifiers. Decision tree, logistic regression and support vector machine classifiers furnished an accuracy of 83.42%, 89.12% and 91.19%, respectively on a dataset of 1203 ultrasound images procured from a medical diagnostic centre in India. Support vector machine has proved to be a better classifier in relation to accuracy (91.19%), precision (97.98%), F1-score (94.49%), specificity (90.90%) and area under curve (0.93) compared to decision tree and logistic regression. This computer aided diagnosis system helps healthcare providers in making efficient and accurate decisions towards treatment plan of patients.