Adaptive Archimedes optimization algorithm trained deep learning for polycystic ovary syndrome detection using ultrasound image

Abstract

Polycystic ovary syndrome (PCOS) disorder is caused by a protracted menstruation cycle that frequently elevated the androgen levels of women in their reproductive age. Insulin resistance affects 50% to 70% of all women with PCOS, and hormone difference contributes the high levels of testosterone that causes the symptoms and signs of PCOS. This work develops a deep learning (DL)-based PCOS diagnosis to address these issues. At the initial stage, the ultra sound image is preprocessed by means of adaptive Wiener filter for noise removal process. The Polycystic ovary (PCO) follicles segmentation process is performed using the Fuzz Local C-Means Clustering (FLICM). Feature extracti­­­on is the neat stage, where the Speeded-Up Robust Feature (SURF), Shape index histogram as well as the statistical features includes variance, mean, kurtosis, entropy and standard deviation are extracted. Furthermore, the PCOS detection is done in the next stage, where a deep Q Net (DQN) is utilized and the parameters of DQN is optimized by the adaptive Archimedes optimization algorithm (AOA). Moreover, the system performance is evaluated using accuracy, sensitivity and specificity parameters with the corresponding values like 0.906, 0.918 and 0.928.