Computer‐Aided Diagnostic System for Detection of Hashimoto Thyroiditis on Ultrasound Images From a Polish Population

Acharya, U. Rajendra; Sree, S. Vinitha; Krishnan, M. Muthu Rama; Molinari, Filippo; ZieleŸnik, Witold; Bardales, Ricardo H.; Witkowska, Agnieszka; Suri, Jasjit S.

doi:10.7863/ultra.33.2.245

Cited by 54 publications

(41 citation statements)

References 35 publications

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“…The decision tree is an effective algorithm for classification of ultrasound images [ 25 , 50 ]. It can learn a classification rule from disorder data.…”

Section: Traditional Ultrasound Cad Systemmentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

164

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The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

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“…The decision tree is an effective algorithm for classification of ultrasound images [ 25 , 50 ]. It can learn a classification rule from disorder data.…”

Section: Traditional Ultrasound Cad Systemmentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

164

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“…However, only a limited number of studies have been performed to examine the diagnostic performance of CAD systems using ultrasound images to identify malignant nodules. 14,[22][23][24] These studies showed the diagnostic accuracies ranging from 72.9% to 100%. Most image datasets used for training only included fewer than 200 thyroid nodules.…”

Section: Discussionmentioning

confidence: 94%

Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

et al. 2020

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Purpose: Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. Methods: The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand-crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. Results: The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. Conclusions: The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system.

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“…Differently, the texture analysis is based on the spatial variation of the pixel intensity, and thus, takes into account, besides the pixels grey level variation, also the relative spatial position between pixels (Baheerathan et al ., ). The mathematical motivation to use GLCM texture analysis is that higher‐order and nonlinear descriptors can better characterize imaging patterns than histogram‐based parameters (Acharya et al ., ).…”

Section: Discussionmentioning

confidence: 97%

Texture analysis of ultrasound images is a sensitive method to follow‐up muscle damage induced by eccentric exercise

Matta

Pereira

Radaelli

et al. 2017

Clin Physio Funct Imaging

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The grey level of co-occurrence matrix (GLCM) is a texture analysis approach accounting for spatial distribution of the pixels from an image and can be a promising method for exercise-induced muscle damage (EIMD) studies. We followed up the time changes of two GLCM texture parameters and echo intensity (EI) on ultrasound images after eccentric contractions. Thirteen untrained women performed two sets of ten elbow flexions eccentric contractions. Ultrasound images were acquired at baseline and 24 h, 48 h, 72 h and 96 h after exercise. Two GLCM texture parameters were calculated for the brachialis muscle: contrast (CON) and correlation (COR). Peak torque, EI, muscle thickness (MT) and soreness were measured. The peak torque and soreness decreased immediately after the intervention in comparison with all the measures. MT increased immediately after the intervention remaining for 72 h (P<0·05). Significant increases (P<0·05) were observed for COR (48, 72 and 96 h) and EI only at 72 and 96 h. The increasing COR represents high similarity between grey levels, which could be observed on US images after few days on eccentric training for elbow flexors.

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Computer‐Aided Diagnostic System for Detection of Hashimoto Thyroiditis on Ultrasound Images From a Polish Population

Cited by 54 publications

References 35 publications

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

Texture analysis of ultrasound images is a sensitive method to follow‐up muscle damage induced by eccentric exercise

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