Machine Learning in Image Analysis and Pattern Recognition

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Image Analysis

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

scholarly journals Machine learning-based image analysis for accelerating the diagnosis of complicated preneoplastic and neoplastic ductal lesions in breast biopsy tissues

2021 ◽  
Author(s):  
Shinya Sato ◽  
Satoshi Maki ◽  
Takashi Yamanaka ◽  
Daisuke Hoshino ◽  
Yukihide Ota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Breast Biopsy

Pressure pattern recognition in buildings using an unsupervised machine-learning algorithm

2021 ◽  
Vol 214 ◽  
pp. 104629
Author(s):  
Bubryur Kim ◽  
N. Yuvaraj ◽  
K.T. Tse ◽  
Dong-Eun Lee ◽  
Gang Hu
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Unsupervised Machine Learning ◽  
Pressure Pattern

scholarly journals Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 642
Author(s):  
Yi-Da Wu ◽  
Ruey-Kai Sheu ◽  
Chih-Wei Chung ◽  
Yen-Ching Wu ◽  
Chiao-Chi Ou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Excellent Agreement ◽  
Antinuclear Antibody ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Observer Agreement ◽  
Supervised Machine Learning ◽  
International Consensus ◽  
Testing Data

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

scholarly journals Image analysis and machine learning based malaria assessment system

2021 ◽  
Author(s):  
Kyle Manning ◽  
Xiaojun Zhai ◽  
Wangyang Yu
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Assessment System

scholarly journals Evaluating User and Machine Learning in Short- and Long-Term Pattern Recognition-Based Myoelectric Control

2021 ◽  
Vol 29 ◽  
pp. 777-785
Author(s):  
Bo Lv ◽  
Guohong Chai ◽  
Xinjun Sheng ◽  
Han Ding ◽  
Xiangyang Zhu
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Myoelectric Control ◽  
Short And Long Term

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

Coding of Experimental Conditions in Microfluidic Droplet Assays Using Colored Beads and Machine Learning Supported Image Analysis

Small ◽  
2018 ◽  
pp. 1802384 ◽  
Author(s):  
Carl‐Magnus Svensson ◽  
Oksana Shvydkiv ◽  
Stefanie Dietrich ◽  
Lisa Mahler ◽  
Thomas Weber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Experimental Conditions

A pattern recognition model for static gestures in malaysian sign language based on machine learning techniques

2021 ◽  
Vol 95 ◽  
pp. 107383
Author(s):  
Ali.H. Alrubayi ◽  
M.A. Ahmed ◽  
A.A. Zaidan ◽  
A.S. Albahri ◽  
B.B. Zaidan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Sign Language ◽  
Machine Learning Techniques ◽  
Recognition Model ◽  
Learning Techniques
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