Deep-GAN: an improved model for thyroid nodule identification and classification
- Resource Type
- Original Paper
- Authors
- Srivastava, Rajshree; Kumar, Pardeep
- Source
- Neural Computing and Applications. 36(14):7685-7704
- Subject
- GAN
Classification
Ultrasound images
Augmentation
Alex-Net
VGG-16
- Language
- English
- ISSN
- 0941-0643
1433-3058
Tailoring a deep convolutional neural network (DCNN) is a tedious and time-consuming task in the field of medical image analysis. In this research paper, Deep-generative adversial neural network (Deep-GAN) based model is proposed using grid search optimization (GSO) technique for identification and classification of thyroid nodule. The main objective of this work is to propose a deep learning (DL) model for the identification and classification of thyroid nodules without user or specialist intervention. The proposed model has gone through four phases namely (i) data acquisition, (ii) pre-processing (iii) data augmentation using GAN technique and (iv) optimization and classification using Deep-GAN model. Two pre-trained architectures namely Alex-Net and Visual Geometry Group (VGG-16) are considered for the identification and classification of thyroid nodule in ultrasonography (USG) images. From the experiment, it is found that Alex-GAN model has shown an improvement of 2 to 4 percentage points in comparison with VGG-GAN model and reported literature on Thyroid digital image database (TDID) public and collected dataset.