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Neural Computing and Applications
Published in: Neural Computing and Applications 14/2024

19-02-2024 | Original Article

Deep-GAN: an improved model for thyroid nodule identification and classification

Authors: Rajshree Srivastava, Pardeep Kumar

Published in: Neural Computing and Applications | Issue 14/2024

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Abstract

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.
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Metadata
Title
Deep-GAN: an improved model for thyroid nodule identification and classification
Authors
Rajshree Srivastava
Pardeep Kumar
Publication date
19-02-2024
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 14/2024
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-024-09492-6

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