Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network

Guo, Minghui; Wang, Kangjian; Liu, Shunlan; Du, Yongzhao; Liu, Peizhong; Q, Su; Lv, Guorong

doi:10.1155/2021/5598001

Cited by 11 publications

(9 citation statements)

References 37 publications

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“…During feature extraction process, the segmented image is passed as to ResNet-18 technique to identify the lesion regions in the ultrasound images [ 23 ]. For extracting deep features from input images, a deep CNN was needed that trained.…”

Section: The Proposed Modelmentioning

confidence: 99%

[Retracted] 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: The Proposed Modelmentioning

confidence: 99%

[Retracted] 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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“…In other tumors, such as esophageal cancer, lung cancer, and prostate cancer, AI based on deep learning has also achieved good detection results [ 21 , 22 , 23 ]. ResNet is a classic residual neural network and has an excellent performance in image classification tasks, including ResNet-18, ResNet-50, and other frameworks [ 24 ]. In the pre-experiments on our data set, we used ResNet-18, ResNet-34, and ResNet-50 for the classification network detection.…”

Section: Discussionmentioning

confidence: 99%

A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor

Luo

et al. 2022

Life

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We aimed to develop an artificial intelligence (AI) diagnosis system for uterine smooth muscle tumors (UMTs) by using deep learning. We analyzed the morphological features of UMTs on whole-slide images (233, 108, and 30 digital slides of leiomyosarcomas, leiomyomas, and smooth muscle tumors of uncertain malignant potential stained with hematoxylin and eosin, respectively). Aperio ImageScope software randomly selected ≥10 areas of the total field of view. Pathologists randomly selected a marked region in each section that was no smaller than the total area of 10 high-power fields in which necrotic, vascular, collagenous, and mitotic areas were labeled. We constructed an automatic identification algorithm for cytological atypia and necrosis by using ResNet and constructed an automatic detection algorithm for mitosis by using YOLOv5. A logical evaluation algorithm was then designed to obtain an automatic UMT diagnostic aid that can “study and synthesize” a pathologist’s experience. The precision, recall, and F1 index reached more than 0.920. The detection network could accurately detect the mitoses (0.913 precision, 0.893 recall). For the prediction ability, the AI system had a precision of 0.90. An AI-assisted system for diagnosing UMTs in routine practice scenarios is feasible and can improve the accuracy and efficiency of diagnosis.

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“…The quality of fetal head ultrasound images was also assessed based on clinical protocols, which have high clinical utility. In 2021, Guo et al 31 proposed a deep-learning method based on residual networks to identify thyroid ultrasound standard planes (TUSP).…”

Section: Related Workmentioning

confidence: 99%

Early Pregnancy Fetal Facial Ultrasound Standard Plane‐Assisted Recognition Algorithm

Xue

et al. 2023

J of Ultrasound Medicine

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Objectives Ultrasound screening during early pregnancy is vital in preventing congenital disabilities. For example, nuchal translucency (NT) thickening is associated with fetal chromosomal abnormalities, particularly trisomy 21 and fetal heart malformations. Obtaining accurate ultrasound standard planes of a fetal face during early pregnancy is the key to subsequent biometry and disease diagnosis. Therefore, we propose a lightweight target detection network for early pregnancy fetal facial ultrasound standard plane recognition and quality assessment. Methods First, a clinical control protocol was developed by ultrasound experts. Second, we constructed a YOLOv4 target detection algorithm based on the backbone network as GhostNet and added attention mechanisms CBAM and CA to the backbone and neck structure. Finally, key anatomical structures in the image were automatically scored according to a clinical control protocol to determine whether they were standard planes. Results We reviewed other detection techniques and found that the proposed method performed well. The average recognition accuracy for six structures was 94.16%, the detection speed was 51 FPS, and the model size was 43.2 MB, and a reduction of 83% compared with the original YOLOv4 model was obtained. The precision for the standard median sagittal plane was 97.20%, and the accuracy for the standard retro‐nasal triangle view was 99.07%. Conclusions The proposed method can better identify standard or non‐standard planes from ultrasound image data, providing a theoretical basis for automatic acquisition of standard planes in the prenatal diagnosis of early pregnancy fetuses.

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Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network

Cited by 11 publications

References 37 publications

[Retracted] Deep Transfer Learning‐Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

[Retracted] Deep Transfer Learning‐Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor

Early Pregnancy Fetal Facial Ultrasound Standard Plane‐Assisted Recognition Algorithm

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