Computer-Assisted Diagnosis of Thyroid Cancer Using Medical Images: A Survey

Anand, Vatsala; Koundal, Deepika

doi:10.1007/978-3-030-29407-6_39

Cited by 12 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At least onethird to forty percent of all cancer cases are preventable merely by not using tobacco, using healthy diet, and being physically active with at least a 30-minute workout daily. Some important studies on thyroid cancer include [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model

Noor

Masood

Sabar

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Cancer is among the major public health problems as well as a burden for Pakistan. About 148,000 new patients are diagnosed with cancer each year, and almost 100,000 patients die due to this fatal disease. Lung, breast, liver, cervical, blood/bone marrow, and oral cancers are the most common cancers in Pakistan. Perhaps smoking, physical inactivity, infections, exposure to toxins, and unhealthy diet are the main factors responsible for the spread of cancer. We preferred a novel four-component mixture model under Bayesian estimation to estimate the average number of incidences and death of both genders in different age groups. For this purpose, we considered 28 different kinds of cancers diagnosed in recent years. Data of registered patients all over Pakistan in the year 2012 were taken from GLOBOCAN. All the patients were divided into 4 age groups and also split based on genders to be applied to the proposed mixture model. Bayesian analysis is performed on the data using a four-component exponential mixture model. Estimators for mixture model parameters are derived under Bayesian procedures using three different priors and two loss functions. Simulation study and graphical representation for the estimates are also presented. It is noted from analysis of real data that the Bayes estimates under LINEX loss assuming Jeffreys’ prior is more efficient for the no. of incidences in male and female. As far as no. of deaths are concerned again, LINEX loss assuming Jeffreys’ prior gives better results for the male population, but for the female population, the best loss function is SELF assuming Jeffreys’ prior.

show abstract

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model

Noor

Masood

Sabar

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…In [ 16 ], the author explains that the thyroid nodule identification, segmentation, classification, and feature extraction methods are shown by providing and discussing noteworthy trends. The benefits and drawbacks of various CAD methods are discussed in detail.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Application Value of Ultrasound Imaging Diagnosis in the Clinical Staging of Thyroid Cancer

Zhang

Sun

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

Thyroid cancer affects 1.3 percent of the population, with rates of occurrence rising in recent years (approximately 2 percent per year). Thyroid cancer is a common endocrine cancer with an annual increase in occurrence. Although the general prognosis for differentiated subtypes is favorable, the rate of mortality linked with thyroid cancer has been steadily progressing. The presence of suspicious thyroid nodules necessitates more diagnostic testing, including laboratory evaluation, additional imaging, and biopsy. For clinical staging and appropriate patient therapy design, accurate diagnosis is necessary. In this paper, we examined the application value of ultrasound imaging diagnosis in the clinical staging of thyroid tumor in this research. The benefit of early diagnosis is determined in this article using ultrasonography reports from Chinese patients. Images of benign and malignant thyroid nodules were collected and annotated in this work, and deep learning-based image recognition and diagnostic system was built utilizing the adaptive wavelet transform-based AdaBoost algorithm (AWT-AA). The system’s efficacy in diagnosing thyroid nodules was assessed, and the use of ultrasound imaging in clinical practice was studied. The variables that had a significant impact on malignant nodules were studied using logistic multiple regression analysis. The sensitivity and specificity of ultrasonography thyroid imaging reporting and data system (TI-RADS) categorization outcomes for benign and malignant tumors were also calculated.

show abstract

“…As an alternative method, specialists diagnose the disease by interpreting the ultrasound (US) images. American Radiology College (ACR) built a risk grading system called “ACR-Thyroid Imaging Reporting and Data System (TIRADS)” for interpretation of US images and categorization of nodules [ 3 ]. As an example, TIRADS 1 represents a benign nodule while TIRADS 5 represents a high-risk malignant nodule [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

Göreke

2023

Interdiscip Sci Comput Life Sci

View full text Add to dashboard Cite

Nodules of thyroid cancer occur in the cells of the thyroid as benign or malign types. Thyroid sonographic images are mostly used for diagnosis of thyroid cancer. The aim of this study is to introduce a computer-aided diagnosis system that can classify the thyroid nodules with high accuracy using the data gathered from ultrasound images. Acquisition and labeling of sub-images were performed by a specialist physician. Then the number of these sub-images were increased using data augmentation methods. Deep features were obtained from the images using a pre-trained deep neural network. The dimensions of the features were reduced and features were improved. The improved features were combined with morphological and texture features. This feature group was rated by a value called similarity coefficient value which was obtained from a similarity coefficient generator module. The nodules were classified as benign or malignant using a multi-layer deep neural network with a pre-weighting layer designed with a novel approach. In this study, a novel multi-layer computer-aided diagnosis system was proposed for thyroid cancer detection. In the first layer of the system, a novel feature extraction method based on the class similarity of images was developed. In the second layer, a novel pre-weighting layer was proposed by modifying the genetic algorithm. The proposed system showed superior performance in different metrics compared to the literature. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s12539-023-00560-4.

show abstract

Computer-Assisted Diagnosis of Thyroid Cancer Using Medical Images: A Survey

Cited by 12 publications

References 34 publications

Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model

Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model

Analysis of the Application Value of Ultrasound Imaging Diagnosis in the Clinical Staging of Thyroid Cancer

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

Contact Info

Product

Resources

About