ITL-CNN: Integrated Transfer Learning-Based Convolution Neural Network for Ultrasound PCOS Image Classification

Abstract

In recent years, Polycystic Ovary Syndrome (PCOS) becomes one of the most prominent research areas, where several researchers are concentrating to improve the accuracy of PCOS classification. It is much difficult to find the presence of PCOS in women with traditional techniques and various researchers are dealt with the problem that affects the accuracy in detecting such symptom. In this paper, we have proposed Integrated Transfer Learning-based Convolutional Neural Network (ITL-CNN) model to improve the classification accuracy for the detection of PCOS using ultrasound images. In this proposed model, we have used active contour with modified Otsu method and Multifactor Dimension Reduction-based GIST feature extractor for improving the performance of the ITL-CNN model. The performance of the proposed model is analyzed using various performance metrics such as accuracy, sensitivity, precision, recall, and F1 score. Furthermore, the results show that the proposed ITL-CNN model outperforms by achieving 98.9% of accuracy when compared with other existing techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Naïve Bayes (NB).