Transfer learning approach in deep neural networks for uterine fibroid detection Online publication date: Tue, 08-Feb-2022
by Sumod Sundar; S. Sumathy
International Journal of Computational Science and Engineering (IJCSE), Vol. 25, No. 1, 2022
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook