Transfer learning approach in deep neural networks for uterine fibroid detection. (4th February 2022)

• Record Type: Journal Article
• Authors: Sundar, Sumod; Sumathy, S.
• Is Part Of: International journal of computational science and engineering. Volume 25:Number 1(2022)
• Page Start: 52
• Page End: 63
• Publication Date: 2022-02-04
• Subjects: convolutional neural network -- CNN -- transfer learning -- deep learning -- uterine fibroid -- inception-V4 -- support vector machine -- SVM -- MRI imaging -- medical imaging -- feature transfer -- object detection; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Convolutional neural network (CNN), a class of deep neural network, takes images as input and automatically extracts features for effective class prediction. The performance of CNN architecture is a major concern while dealing with fewer data. Traditional CNN architectures like ImageNet, AlexNet, GoogleNet are trained with a large quantity of data. Transfer learning is applied in order to combine pre-trained models such as Inception-V4 and support vector machine (SVM) for better performance with fewer data. The goal of the proposed approach is to efficiently detect fibroid presence in uterus MRI images. Features of these images are extracted using initial layers of Inception-V4 and are transferred to SVM during training. Several combinations on various network classifiers are tested and the performance metrics are evaluated. The proposed model attained an accuracy of 81.05% with a U-Kappa score of 0.402 on predicting fibroid images and 0.25% accuracy improvement compared with ResNet based model.