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

19.02.2024 | Original Article

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

verfasst von: Rajshree Srivastava, Pardeep Kumar

Erschienen in: Neural Computing and Applications | Ausgabe 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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Metadaten
Titel
Deep-GAN: an improved model for thyroid nodule identification and classification
verfasst von
Rajshree Srivastava
Pardeep Kumar
Publikationsdatum
19.02.2024
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 14/2024
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-024-09492-6

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