scholarly journals Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6110
Author(s):  
Elmer Jeto Gomes Ataide ◽  
Nikhila Ponugoti ◽  
Alfredo Illanes ◽  
Simone Schenke ◽  
Michael Kreissl ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Diagnostic Process ◽  
Cad System ◽  
Physician Decision Support ◽  
Visual Characteristics ◽  
Nodule Classification

The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size, echogenicity, calcifications, margins, and vascularity. This work aims to reduce subjectivity in the current diagnostic process by using geometric and morphological (G-M) features that represent the visual characteristics of thyroid nodules to provide physicians with decision support. A total of 27 G-M features were extracted from images obtained from an open-access US thyroid nodule image database. 11 significant features in accordance with TIRADS were selected from this global feature set. Each feature was labeled (0 = benign and 1 = malignant) and the performance of the selected features was evaluated using machine learning (ML). G-M features together with ML resulted in the classification of thyroid nodules with a high accuracy, sensitivity and specificity. The results obtained here were compared against state-of the-art methods and perform significantly well in comparison. Furthermore, this method can act as a computer aided diagnostic (CAD) system for physicians by providing them with a validation of the TIRADS visual characteristics used for the classification of thyroid nodules in US images.

scholarly journals Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 1211-1215
Keyword(s):  
Deep Learning ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Second Opinion ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Cad System ◽  
Improve Accuracy

Ultrasound scanning is most excellent significant diagnosis techniques utilized for thyroid nodules identification. A thyroid nodule is unnecessary cells that can develop in your base of neck which can be normal or cancerous. Many Computer added diagnosis systems (CAD) have been developed as a second opinion for radiologist. The thyroid nodules classification using machine learning and deep learning approach is latest trend which is using to improve accuracy for differentiation of thyroid nodules from benign and malignant type. In this paper we review the most recent work on CAD system which uses different feature extraction technique and classifier used for thyroid nodules classification with deep learning approach. This paper we illustrate the result obtained by these studies and highlight the limitation of each proposed methods. Moreover we summarize convolution neural network (CNN) architecture for classification of thyroid nodule. This literature review is meant at researcher but it also useful for radiologist who is interesting in CAD tool in ultrasound imaging for second opinion.

scholarly journals Comparison of TI-RADS classification with FNAC for the Diagnosis of Thyroid Nodules

2020 ◽  
Vol 9 (2) ◽  
pp. 129-133
Author(s):  
Naushaba Malik ◽  
Maryam Rauf ◽  
Ghazala Malik
Keyword(s):  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Needle Aspiration ◽  
Clinical Approach ◽  
Pathology Laboratory ◽  
Class V ◽  
Thyroid Imaging ◽  
Female Predominance ◽  
Identification Techniques

Background: Thyroid nodules are very common and its prevalence is largely dependent on the identification techniques. Recently high-resolution ultrasound thyroid imaging has paved the way for significant transformation in clinical approach to thyroid nodule. This study aimed to determine the efficacy of TI-RADS classification and its association with FNAC findings in thyroid lesions. Material and Methods: This prospective study was carried out in the Department of Radiology of Islamabad Diagnostic Centre, Islamabad for a period of 6 months from 6th January 2018 to 6th July, 2018. All male and female patients presenting with thyroid nodules were selected for the study. Ultrasound neck was performed with high frequency linear probes. Ultrasonography findings were classified according to Thyroid Imaging Reporting and Data System (TI-RADS) classification, as defined by Horvath et al. Patients with TI-RADS II-V were scheduled for US-guided Fine Needle Aspiration (FNA). All the specimens were sent to pathology laboratory for cytology (FNAC). Results: Total 123 patients of thyroid nodules were studied. Mean age of the patients was 45.78 ± 13.11 years, with a female predominance (73.2%). A significant association was seen between TI-RADS classification of thyroid nodules and findings on cytology. Thyroid nodules with TI-RADS II, III and IV a classification showed benign cytological findings, while TI-RADS class V had a significant association with malignant findings on cytology (P=0.001). Conclusions: TI-RADS classification is a reliable modality in differentiating benign thyroid nodules from malignant ones and circumvent the need for FNAC in every case with a thyroid nodule.

Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules

2019 ◽  
Vol 113 ◽  
pp. 251-257 ◽  
Author(s):  
Fu-sheng Ouyang ◽  
Bao-liang Guo ◽  
Li-zhu Ouyang ◽  
Zi-wei Liu ◽  
Shao-jia Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thyroid Nodules ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Linear And Nonlinear

scholarly journals Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 9 (4) ◽  
pp. 186-193
Author(s):  
Lei Xu ◽  
Junling Gao ◽  
Quan Wang ◽  
Jichao Yin ◽  
Pengfei Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79–0.92], specificity 0.85 [95% CI 0.77–0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91–56.20]; deep learning: sensitivity 0.89 [95% CI 0.81–0.93], specificity 0.84 [95% CI 0.75–0.90], DOR 40.87 [95% CI 18.13–92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78–0.93] vs. 0.87 [95% CI 0.85–0.89], specificity 0.85 [95% CI 0.76–0.91] vs. 0.87 [95% CI 0.81–0.91], DOR 40.12 [95% CI 15.58–103.33] vs. DOR 44.88 [95% CI 30.71–65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

scholarly journals Extreme Learning Machine for Thyroid Nodule Classification with Graph Cluster Ant Colony Optimization based Feature Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 1478-1488
Keyword(s):  
Feature Selection ◽  
Thyroid Nodule ◽  
Ant Colony Optimization ◽  
Extreme Learning Machine ◽  
Thyroid Nodules ◽  
Computation Time ◽  
Ant Colony ◽  
Learning Machine ◽  
Benign Nodules ◽  
Nodule Classification

Thyroid nodule is defined as an endocrine malignancy that occurs in humans due to abnormal growth of cells. Recently, an increasing level of thyroid incidence has been identified worldwide. Thus, it is necessary to detect the nodules at an early stage. Ultrasonography is an important tool that is utilized for the detection as well as differentiation of malignant thyroid nodules from benign nodules. The nodules in ultrasound appear in different heterogenic forms, which are difficult to differentiate by the physicians. Further, large number of features available in US characteristics increases the computation time as well as complexity of classification. In this paper, GraphClustering Ant Colony Optimization based Extreme Learning Machine approach is proposed to achieve efficient diagnosis of thyroid nodules. It will enhance thyroid nodule classification by selecting only the optimal features and further using it for improving the function of classifier. The main goal of this technique is to differentiate the malignant nodules from the benign nodules. The performance of both feature selection and classification are evaluated through parameters such as accuracy, AUC, sensitivity and specificity. From the experimental results, it is revealed that the proposed method is significantly better than the existing methods. Thus, it is considered to be an effective tool for diagnosing the thyroid nodules with less complexity and reduced computation time.

scholarly journals An algorithm for characterizing skin moles using image processing and machine learning

2021 ◽  
Vol 11 (4) ◽  
pp. 3539
Author(s):  
Zaid Sanchez ◽  
Alicia Alva ◽  
Mirko Zimic ◽  
Christian del Carpio
Keyword(s):  
Machine Learning ◽  
Skin Cancer ◽  
Low Income ◽  
Support Vector ◽  
Classification Algorithms ◽  
Extraction Algorithm ◽  
High Incidence ◽  
Advanced Stages ◽  
Visual Characteristics

<a name="_Hlk65806330"></a><span>Melanoma, the most serious type of skin cancer, forms in cells (melanocytes) that produce melanin, the pigment that gives color to the skin. There are low-income regions that lack specialized dermatologists, causing skin cancer to be diagnosed in advanced stages. In Peru, in high Andean communities with low resources, the problem is aggravated by the high incidence of ultraviolet radiation and lack of medical resources to make the diagnosis. Normally, mole images are obtained from dermatoscopes. The present work seeks to use mole images obtained from smartphones to make the classification of them as suspected or not suspected of being melanoma, by means of a feature extraction algorithm. The first step is to make color and lighting corrections. After this, the image is segmented using the K-Means algorithm, and we obtain the areas of the mole and skin. With the segmented mole we proceed to extract the main visual characteristics and then use classification algorithms such as support vector machine (SVM), random forest and naïve bayes, which obtained an accuracy of 0.9473, 0.7368 and 0.6842, respectively. These results show that it is possible to use images obtained from smartphones to develop a classification algorithm with 94.73% accuracy to detect melanoma in skin moles.</span>

scholarly journals Detection and classification of thyroid nodule using Shearlet coefficients and support vector machine

2017 ◽  
Vol 6 (3) ◽  
pp. 50
Author(s):  
Nanda S. ◽  
Sukumar M.
Keyword(s):  
Support Vector Machine ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Textural Features ◽  
Ultrasound Images ◽  
K Nearest Neighbor ◽  
Benign Nodules

Thyroid nodules have diversified internal components and dissimilar echo patterns in ultrasound images. Textural features are used to characterize these echo patterns. This paper presents a classification scheme that uses shearlet transform based textural features for the classification of thyroid nodules in ultrasound images. The study comprised of 60 thyroid ultrasound images (30 with benign nodules and 30 with malignant nodules). Total of 22 features are extracted. Support vector machine (SVM) and K nearest neighbor (KNN) are used to differentiate benign and malignant nodules. The diagnostic sensitivity, specificity, F1_score and accuracy of both the classifiers are calculated. A comparative study has been carried out with respect to their performances. The sensitivity of SVM with radial basis function (RBF) kernel is 100% as compared to that of KNN with 96.33%. The proposed features can increase the accuracy of the classifier and decrease the rate of misdiagnosis in thyroid nodule classification.

scholarly journals Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Binny Naik ◽  
Ashir Mehta ◽  
Manan Shah
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Single Photon ◽  
Photon Emission ◽  
Diagnostic Process ◽  
Support Vector ◽  
Emission Computed Tomography ◽  
Effective Diagnosis

Abstract Alzheimer’s disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.

scholarly journals Artificial intelligence applied to support the breast cancer diagnosis

2021 ◽  
Author(s):  
Woldson Leonne Pereira Gomes ◽  
Antonio Silveira
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Cancer Diagnosis ◽  
Final Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cad System ◽  
Non Melanoma Skin Cancer ◽  
Learning Techniques

Breast cancer is a more common neoplasm among women (not considering non-melanoma skin cancer). The estimate for the coming years is still growing and poses a threat to human health. Currently, the methods used in the diagnosis of breast cancer are performed through analysis of mammography images. Allowed, an analysis made by two specialists, which are subject to errors due to factors such as fatigue and lack of capacity. Not only the factor of human errors in diagnoses, certainly the long periods of time until the final diagnosis is another factor to be taken into account, because cancer is a progressive disease over time. In this sense, the present work applied a solution through the automatic classification of mammography images, in order to determine as normal or cancer. In addition, for simulations, two machine learning techniques were added independently, as they can eventually serve as a support in the diagnosis of breast cancer, that is, a CAD system, which means “computer-aided diagnosis”. As machine learning techniques applied for classification referenced as convolutional neural networks and support vector machines. Subsequently, the construction of the classification algorithms, they were subjected to the testing phase, which was found to be more than 85% accurate in the classification of mammography images.

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