A generic deep learning framework to classify thyroid and breast lesions in ultrasound images

Zhu, Yicheng; AlZoubi, Alaa; Jassim, Sabah; Jiang, Quan; Zhang, Yuan; Wang, Yongbing; Ye, Xian-De; Du, Hongbo

doi:10.1016/j.ultras.2020.106300

Cited by 73 publications

(80 citation statements)

References 35 publications

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“…Zhu et al [ 19 ] developed an automated system for categorizing thyroid and breast cancers in ultrasound images with DCNN. Particularly, we proposed a generic DCNN framework using TL and the similar structural parameter settings for training model to thyroid and breast lesions (TNet and BNet) correspondingly and test the feasibility of generic model using ultrasound images gathered from medical practice.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

Althobaiti

Ashour

Alhindi

et al. 2022

BioMed Research International

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The rapid development of technologies in biomedical research has enriched and broadened the range of medical equipment. Magnetic resonance imaging, ultrasonic imaging, and optical imaging have been discovered by diverse research communities to design multimodal systems, which is essential for biomedical applications. One of the important tools is photoacoustic multimodal imaging (PAMI) which combines the concepts of optics and ultrasonic systems. At the same time, earlier detection of breast cancer becomes essential to reduce mortality. The recent advancements of deep learning (DL) models enable detection and classification the breast cancer using biomedical images. This article introduces a novel social engineering optimization with deep transfer learning-based breast cancer detection and classification (SEODTL-BDC) model using PAI. The intention of the SEODTL-BDC technique is to detect and categorize the presence of breast cancer using ultrasound images. Primarily, bilateral filtering (BF) is applied as an image preprocessing technique to remove noise. Besides, a lightweight LEDNet model is employed for the segmentation of biomedical images. In addition, residual network (ResNet-18) model can be utilized as a feature extractor. Finally, SEO with recurrent neural network (RNN) model, named SEO-RNN classifier, is applied to allot proper class labels to the biomedical images. The performance validation of the SEODTL-BDC technique is carried out using benchmark dataset and the experimental outcomes pointed out the supremacy of the SEODTL-BDC approach over the existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

Althobaiti

Ashour

Alhindi

et al. 2022

BioMed Research International

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“…Therefore, the mirroring generates the images of different classes, which increases the complexity of training data and difficulties of model training. As shown in [ 57 , 58 ], singular value decomposition (SVD) can generate the images of various styles, which can also produce the images of different classes, and it can also increase the complexity of training data and difficulties of model training. Therefore, we use our simple data augmentation method.…”

Section: Discussionmentioning

confidence: 99%

Restoration of Motion Blurred Image by Modified DeblurGAN for Enhancing the Accuracies of Finger-Vein Recognition

Choi

Hong

Owais

et al. 2021

Sensors

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Among many available biometrics identification methods, finger-vein recognition has an advantage that is difficult to counterfeit, as finger veins are located under the skin, and high user convenience as a non-invasive image capturing device is used for recognition. However, blurring can occur when acquiring finger-vein images, and such blur can be mainly categorized into three types. First, skin scattering blur due to light scattering in the skin layer; second, optical blur occurs due to lens focus mismatching; and third, motion blur exists due to finger movements. Blurred images generated in these kinds of blur can significantly reduce finger-vein recognition performance. Therefore, restoration of blurred finger-vein images is necessary. Most of the previous studies have addressed the restoration method of skin scattering blurred images and some of the studies have addressed the restoration method of optically blurred images. However, there has been no research on restoration methods of motion blurred finger-vein images that can occur in actual environments. To address this problem, this study proposes a new method for improving the finger-vein recognition performance by restoring motion blurred finger-vein images using a modified deblur generative adversarial network (modified DeblurGAN). Based on an experiment conducted using two open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger-vein database and Hong Kong Polytechnic University finger-image database version 1, the proposed method demonstrates outstanding performance that is better than those obtained using state-of-the-art methods.

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“…A mass-level classification method enabled the construction of an ensemble network by combining VGG and ResNet to classify a given mass using all views [68]. Considering that both thyroid and breast cancers exhibit several similar high-frequency US characteristics, Zhu et al developed a generic VGG-based framework to classify thyroid and breast lesions in US imaging [69]. The model that was constructed with features that were extracted from all three transferred models achieved the highest overall performance [70].…”

Section: Ifss-netmentioning

confidence: 99%

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Komatsu

Sakai

Dozen³

et al. 2021

Biomedicines

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Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.

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A generic deep learning framework to classify thyroid and breast lesions in ultrasound images

Cited by 73 publications

References 35 publications

Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

Restoration of Motion Blurred Image by Modified DeblurGAN for Enhancing the Accuracies of Finger-Vein Recognition

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

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