Diagnosis of Thyroid Diseases Using SPECT Images Based on Convolutional Neural Network

Ma, Liyong; Ma, Chao; Liu, Yuejun; Wang, Xugang; Xie, Wei

doi:10.1166/jmihi.2018.2493

Cited by 14 publications

(10 citation statements)

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“…This ability to automatically discover features makes CNN achieve better results than traditional methods in many applications such as disease diagnosis based on medical image classification and lesion area segmentation. CNN is also used in a variety of medical imaging [14–16], such as ultrasound, CT, MRI, medical optics images, photoacoustic imaging, and so on.…”

Section: Related Workmentioning

confidence: 99%

Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization

Liu

et al. 2019

Computational Intelligence and Neuroscience

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Thyroid disease has now become the second largest disease in the endocrine field; SPECT imaging is particularly important for the clinical diagnosis of thyroid diseases. However, there is little research on the application of SPECT images in the computer-aided diagnosis of thyroid diseases based on machine learning methods. A convolutional neural network with optimization-based computer-aided diagnosis of thyroid diseases using SPECT images is developed. Three categories of diseases are considered, and they are Graves' disease, Hashimoto disease, and subacute thyroiditis. A modified DenseNet architecture of convolutional neural network is employed, and the training method is improved. The architecture is modified by adding the trainable weight parameters to each skip connection in DenseNet. And the training method is improved by optimizing the learning rate with flower pollination algorithm for network training. Experimental results demonstrate that the proposed method of convolutional neural network is efficient for the diagnosis of thyroid diseases with SPECT images, and it has superior performance than other CNN methods.

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Section: Related Workmentioning

confidence: 99%

Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization

Liu

et al. 2019

Computational Intelligence and Neuroscience

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“…Razia et al (9) used the UCI dataset to compare MLP and radial basis function performances. Ma et al, (10,11) proposed a convolutional neural network according to the computer-aided diagnostic method for thyroid diseases using the single-photon emission computerized tomography (SPECT) images.…”

Section: Grouping Methods Can Minimize Fault Diagnosis Rates Thatmentioning

confidence: 99%

Comparison of the Particle Swarm Optimization with the Genetic Algorithms as a Training for Multilayer Perceptron Technique to Diagnose Thyroid Functional Disease

Sharifi

Alizadeh

2020

Shiraz E-Med J

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Background: Thyroid is a gland that controls body key functions. Since thyroid hormones are responsible for controlling metabolism, the thyroid glands are one of the major organs in the body. Disruption of the glands causes thyroid disease, which is one of the most common endocrine diseases worldwide. Early diagnosis of thyroid disease is difficult since early symptoms are easily confused with those of other illnesses. In recent years, the employment of computer techniques to utilize data mining and intelligent algorithms accelerates the early diagnosis of the disease. Objectives: The current study aimed at evaluating the role of the new automatic method according to a multilayer perceptron (MLP) in the diagnosis of thyroid disease. Methods: The study aimed at comparing the particle swarm optimization (PSO) with the genetic algorithms (GA) as training for MLP technique used to diagnose thyroid functional disease. The data were collected for three classes: 150 cases of euthyroidism, 30 cases of hypothyroidism, and 35 cases of hyperthyroidism. MLP was used to elucidate the pattern in the data and species responsible for separating the classes. Furthermore, improved PSO and GAs were used to train the system, and the sensitivity and specificity of the model were studied in terms of accuracy, sensitivity, and specificity. All analyses were performed using MATLAB software. Results: For the proposed model, the simulations results showed that the GA algorithm had a higher performance than the PSO algorithm in the diagnosis of functional thyroid disease, and the means of classification accuracy, sensitivity, and specificity were 95%, 96%, and 96%, respectively. Conclusions: The results of real data indicated that the GA-MLP can be used with high diagnostic accuracy as an effective tool to clinically diagnose thyroid functional disease. The current study was a step towards prototype system development of the classification of knowledge in this area with a much lower computational cost.

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“…Thyroid gland is essential for segregating the thyroid hormones which helps to regulate the various functional activities in human body [6]. This thyroid gland supports human organs development which is also responsible for various hormones balance activity functionalities [7]. Fig.1 shows the thyroid gland in human neck.…”

Section: Introductionmentioning

confidence: 99%

Detection of Abnormal Tumor Regions in Ultrasonic Thyroid Images using SVM Classification Method

Shankarlal,

Sathya

2020

IJEAT

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Detection of tumor or abnormal regions in thyroid gland is difficult task in human. The following methods are presently used for detecting the abnormal regions in thyroid gland as blood test, sample testing from thyroid gland and image processing method. This paper develops a methodology to detect the tumor regions in thyroid images using image registration and image enhancement technique. The Support Vector Machine (SVM) classifier is operated in two modes as training pattern generation and testing mode. The generation of training pattern from both normal and abnormal ultrasonic thyroid images. This proposed method for thyroid tumor region detection obtains 96.54% of sensitivity, 97.57% of specificity and 98.56% of average tumor segmentation accuracy.

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Diagnosis of Thyroid Diseases Using SPECT Images Based on Convolutional Neural Network

Cited by 14 publications

References 0 publications

Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization

Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization

Comparison of the Particle Swarm Optimization with the Genetic Algorithms as a Training for Multilayer Perceptron Technique to Diagnose Thyroid Functional Disease

Detection of Abnormal Tumor Regions in Ultrasonic Thyroid Images using SVM Classification Method

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