{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T14:34:04Z","timestamp":1771338844692,"version":"3.50.1"},"reference-count":74,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2024,11,15]]},"abstract":"<jats:p>INTRODUCTION: Ultrasound in conjunction with mammography imaging, plays a vital role in the early detection and diagnosis of breast cancer. However, speckle noise affects medical ultrasound images and degrades visual radiological interpretation. Speckle carries information about the interactions of the ultrasound pulse with the tissue microstructure, which generally causes several difficulties in identifying malignant and benign regions. The application of deep learning in image denoising has gained more attention in recent years. OBJECTIVES: The main objective of this work is to reduce speckle noise while preserving features and details in breast ultrasound images using GAN models. METHODS: We proposed two GANs models (Conditional GAN and Wasserstein GAN) for speckle-denoising public breast ultrasound databases: BUSI, DATASET A, AND UDIAT (DATASET B). The Conditional GAN model was trained using the Unet architecture, and the WGAN model was trained using the Resnet architecture. The image quality results in both algorithms were measured by Peak Signal to Noise Ratio (PSNR, 35\u201340 dB) and Structural Similarity Index (SSIM, 0.90\u20130.95) standard values. RESULTS: The experimental analysis clearly shows that the Conditional GAN model achieves better breast ultrasound despeckling performance over the datasets in terms of PSNR = 38.18 dB and SSIM = 0.96 with respect to the WGAN model (PSNR = 33.0068 dB and SSIM = 0.91) on the small ultrasound training datasets. CONCLUSIONS: The observed performance differences between CGAN and WGAN will help to better implement new tasks in a computer-aided detection\/diagnosis (CAD) system. In future work, these data can be used as CAD input training for image classification, reducing overfitting and improving the performance and accuracy of deep convolutional algorithms.<\/jats:p>","DOI":"10.3233\/ida-230631","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T16:12:51Z","timestamp":1709309571000},"page":"1661-1678","source":"Crossref","is-referenced-by-count":3,"title":["Ultrasound breast images denoising using generative adversarial networks (GANs)"],"prefix":"10.1177","volume":"28","author":[{"given":"Yuliana","family":"Jim\u00e9nez-Gaona","sequence":"first","affiliation":[{"name":"Departamento de Qu\u00edmica y Ciencias Exactas, Universidad T\u00e9cnica Particular de Loja, Loja, Ecuador"},{"name":"Instituto de Instrumentacion Para la Imagen Molecular I3M,"},{"name":"Medihospital, Loja-Ecuador, Av. Eugenio Espejo y Shuaras 07 39 50 600, Ecuador"}]},{"given":"Mar\u00eda Jos\u00e9","family":"Rodr\u00edguez-Alvarez","sequence":"additional","affiliation":[{"name":"Instituto de Instrumentacion Para la Imagen Molecular I3M,"}]},{"given":"L\u00edder","family":"Escudero","sequence":"additional","affiliation":[{"name":"Medihospital, Loja-Ecuador, Av. Eugenio Espejo y Shuaras 07 39 50 600, Ecuador"}]},{"given":"Carlos","family":"Sandoval","sequence":"additional","affiliation":[{"name":"Medihospital, Loja-Ecuador, Av. Eugenio Espejo y Shuaras 07 39 50 600, Ecuador"}]},{"given":"Vasudevan","family":"Lakshminarayanan","sequence":"additional","affiliation":[{"name":"Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science,"},{"name":"Department of Systems Design Engineering, Physics, and Electrical and Computer Engineering,"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-230631_ref1","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s12149-022-01719-7","article-title":"Deep learning for image classification in dedicated breast positron emission tomography (dbPET)","volume":"36","author":"Satoh","year":"2022","journal-title":"Ann Nucl Med"},{"key":"10.3233\/IDA-230631_ref2","doi-asserted-by":"crossref","first-page":"17847","DOI":"10.1038\/s41598-019-54371-z","article-title":"Machine learning approaches to radiogenomics of breast cancer using low-dose perfusion computed tomography: Predicting prognostic biomarkers and molecular subtypes","volume":"9","author":"Park","year":"2019","journal-title":"Scientific Reports"},{"key":"10.3233\/IDA-230631_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40644-019-0252-2","article-title":"Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution","volume":"19","author":"Ji","year":"2019","journal-title":"Cancer Imaging"},{"issue":"5","key":"10.3233\/IDA-230631_ref4","doi-asserted-by":"crossref","first-page":"4701","DOI":"10.1016\/j.aej.2021.03.048","article-title":"Deep learning in mammography images segmentation and classification: Automated CNN approach","volume":"60","author":"Salama","year":"2021","journal-title":"Alexandria Engineering Journal"},{"key":"10.3233\/IDA-230631_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ultras.2018.07.006","article-title":"Medical breast ultrasound image segmentation by machine learning","volume":"91","author":"Xu","year":"2019","journal-title":"Ultrasonics"},{"key":"10.3233\/IDA-230631_ref8","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s11075-017-0386-x","article-title":"Speckle noise removal in ultrasound images by first-and second-order total variation","volume":"78","author":"Wang","year":"2018","journal-title":"Numerical Algorithms"},{"issue":"4","key":"10.3233\/IDA-230631_ref9","first-page":"139","article-title":"Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging","volume":"40","author":"Kaji","year":"2020","journal-title":"Igaku Butsuri: Nihon Igaku Butsuri Gakkai Kikanshi = Japanese Journal of Medical Physics: an Official Journal of Japan Society of Medical Physics"},{"key":"10.3233\/IDA-230631_ref10","doi-asserted-by":"crossref","unstructured":"I. 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