An improved pix2pix GAN model to enhance thyroid nodule segmentation
Thyroid nodules are a type of lesion, which doctors often need advanced diagnostic tools to detect and conduct followup diagnoses. Supervised deep learning techniques, particularly generative adversarial networks (GANs), have been used to extract essential features, detect nodules and generate thyro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Engineering & Technology Publishing
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118552/ http://psasir.upm.edu.my/id/eprint/118552/1/118552.pdf |
| _version_ | 1848867542427238400 |
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| author | Yaakob, Razali Abd Alwahab, Al Shahad Huda Fawzi Abu Hassan, Hasyma Mohd Sharef, Nurfadhlina Hamdan, Hazlina |
| author_facet | Yaakob, Razali Abd Alwahab, Al Shahad Huda Fawzi Abu Hassan, Hasyma Mohd Sharef, Nurfadhlina Hamdan, Hazlina |
| author_sort | Yaakob, Razali |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Thyroid nodules are a type of lesion, which doctors often need advanced diagnostic tools to detect and conduct followup diagnoses. Supervised deep learning techniques, particularly generative adversarial networks (GANs), have been used to extract essential features, detect nodules and generate thyroid masks. However, these approaches suffer significant challenges in obtaining training data due to the high cost of identifying the cancer area and mode collapse during training. Therefore, this study proposed an improvement to one GAN model, namely, the pixel-to-pixel (pix2pix) model, for thyroid nodule segmentation, where the generator was incorporated with a supervised loss function to address instabilities during GAN training. The model used a generator with an encoder–decoder structure inspired by U-Net architecture to produce the mask. The discriminator of the model consists of a multilayered convolutional neural network (CNN) to compare the real and generated masks. In addition, three loss functions, namely, binary cross-entropy loss, soft dice loss and Jaccard loss, combined with loss GAN were used to stabilise the GAN model. Based on the results, the proposed model achieved 97% detection accuracy of the cancer area from the ultrasound thyroid nodule images and segmented it using the stabilised model with a generator loss function value of 0.5. In short, this study showed that the improved pix2pix model produced greater flexibility in nodule segmentation accuracy compared with semisupervised segmentation models. |
| first_indexed | 2025-11-15T14:38:09Z |
| format | Article |
| id | upm-118552 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:38:09Z |
| publishDate | 2024 |
| publisher | Engineering & Technology Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1185522025-07-16T23:45:48Z http://psasir.upm.edu.my/id/eprint/118552/ An improved pix2pix GAN model to enhance thyroid nodule segmentation Yaakob, Razali Abd Alwahab, Al Shahad Huda Fawzi Abu Hassan, Hasyma Mohd Sharef, Nurfadhlina Hamdan, Hazlina Thyroid nodules are a type of lesion, which doctors often need advanced diagnostic tools to detect and conduct followup diagnoses. Supervised deep learning techniques, particularly generative adversarial networks (GANs), have been used to extract essential features, detect nodules and generate thyroid masks. However, these approaches suffer significant challenges in obtaining training data due to the high cost of identifying the cancer area and mode collapse during training. Therefore, this study proposed an improvement to one GAN model, namely, the pixel-to-pixel (pix2pix) model, for thyroid nodule segmentation, where the generator was incorporated with a supervised loss function to address instabilities during GAN training. The model used a generator with an encoder–decoder structure inspired by U-Net architecture to produce the mask. The discriminator of the model consists of a multilayered convolutional neural network (CNN) to compare the real and generated masks. In addition, three loss functions, namely, binary cross-entropy loss, soft dice loss and Jaccard loss, combined with loss GAN were used to stabilise the GAN model. Based on the results, the proposed model achieved 97% detection accuracy of the cancer area from the ultrasound thyroid nodule images and segmented it using the stabilised model with a generator loss function value of 0.5. In short, this study showed that the improved pix2pix model produced greater flexibility in nodule segmentation accuracy compared with semisupervised segmentation models. Engineering & Technology Publishing 2024-01-09 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/118552/1/118552.pdf Yaakob, Razali and Abd Alwahab, Al Shahad Huda Fawzi and Abu Hassan, Hasyma and Mohd Sharef, Nurfadhlina and Hamdan, Hazlina (2024) An improved pix2pix GAN model to enhance thyroid nodule segmentation. Journal of Advances in Information Technology, 16 (1). pp. 37-48. ISSN 1798-2340 https://www.jait.us/show-249-1624-1.html 10.12720/jait.16.1.37-48 |
| spellingShingle | Yaakob, Razali Abd Alwahab, Al Shahad Huda Fawzi Abu Hassan, Hasyma Mohd Sharef, Nurfadhlina Hamdan, Hazlina An improved pix2pix GAN model to enhance thyroid nodule segmentation |
| title | An improved pix2pix GAN model to enhance thyroid nodule segmentation |
| title_full | An improved pix2pix GAN model to enhance thyroid nodule segmentation |
| title_fullStr | An improved pix2pix GAN model to enhance thyroid nodule segmentation |
| title_full_unstemmed | An improved pix2pix GAN model to enhance thyroid nodule segmentation |
| title_short | An improved pix2pix GAN model to enhance thyroid nodule segmentation |
| title_sort | improved pix2pix gan model to enhance thyroid nodule segmentation |
| url | http://psasir.upm.edu.my/id/eprint/118552/ http://psasir.upm.edu.my/id/eprint/118552/ http://psasir.upm.edu.my/id/eprint/118552/ http://psasir.upm.edu.my/id/eprint/118552/1/118552.pdf |