Semantic segmentation and classification of polycystic ovarian disease using attention UNet, Pyspark, and ensemble learning model

Abstract

Ovarian abnormality like polycystic ovarian disease (PCOD) is one of the most common diseases among women worldwide. PCOD not only has an impact on infertility but also hurts the psychological well‐being of women affecting their quality of life. In this study, a two‐class pattern learning problem is designed for the classification of PCOD. In total, 37 clinical parameters and abdominal ultrasound images of women are collected under the proper ethical protocol. Using only clinical data, an accuracy of 93.7% is obtained using Random Forest as the classifier which is further improved to 95.54% by using a Randomized Search CV during Random Forest classification. The ultrasound images are classified using the proposed Attention‐UNet architecture and a mean Dice score of 0.945 is obtained indicating more accurate segmentation. The segmented images are passed through the state‐of‐the‐art EfficientNet B6 for the classification of PCOS and non‐PCOS and recorded an accuracy of 95.47%. Using big data architecture Pyspark, the performance is further enhanced to 96.8% and 96.3% for clinical and ultrasound images respectively along with the reduced computational speed. The results of these classifiers are then used to create metadata and a customized Artificial Neural Network is applied for the final prediction of PCOD and non‐PCOD. From the results, it can be seen that the stacking model outperformed with an accuracy of 98.12% when compared to the single classifier. Our proposed method has very good performance with less computation, contributing a new architecture to evaluate PCOD and hence helping to improve the wellness of women.