scholarly journals Artificial intelligence for thyroid nodule ultrasound image analysis

2020 ◽  
Vol 5 ◽  
pp. 8-8
Author(s):  
Young Jun Chai ◽  
Junho Song ◽  
Mohammad Shaear ◽  
Ka Hee Yi
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Thyroid Nodule ◽  
Ultrasound Image

Efficient and Effective Ultrasound Image Analysis Scheme for Thyroid Nodule Detection

2007 ◽  
pp. 1052-1060 ◽  
Author(s):  
Eystratios G. Keramidas ◽  
Dimitris K. Iakovidis ◽  
Dimitris Maroulis ◽  
Stavros Karkanis
Keyword(s):  
Image Analysis ◽  
Thyroid Nodule ◽  
Ultrasound Image ◽  
Analysis Scheme ◽  
Nodule Detection

scholarly journals Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1822 ◽  
Author(s):  
Dat Tien Nguyen ◽  
Jin Kyu Kang ◽  
Tuyen Danh Pham ◽  
Ganbayar Batchuluun ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Thyroid Nodule ◽  
State Of The Art ◽  
Ultrasound Image ◽  
The State ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Training Samples ◽  
Frequency Domains

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

Press “A” for Artificial Intelligence in Agriculture: A Review

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Yogesh Awasthi
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Significant Role ◽  
Pattern Analysis ◽  
Cost Effective ◽  
Predictive Analysis ◽  
Accurate Information ◽  
Digital Era ◽  
Learning Techniques ◽  
Crucial Information

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.

Artificial Intelligence for Document Image Analysis

2019 ◽  
pp. 1-14
Author(s):  
Himadri Mukherjee ◽  
Payel Rakshit ◽  
Ankita Dhar ◽  
Sk Md Obaidullah ◽  
KC Santosh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Document Image ◽  
Document Image Analysis
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