scholarly journals Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Abdullah ◽  
Muhammad Hameed Siddiqi ◽  
Yousef Salamah Alhwaiti ◽  
Ibrahim Alrashdi ◽  
Amjad Ali ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Human Heart ◽  
Automatic Segmentation ◽  
Ensemble Classification ◽  
Machine Learning Techniques ◽  
Computer Assisted ◽  
Time Saving ◽  
Significant Information ◽  
Heart Blood

Heart angiography is a test in which the concerned medical specialist identifies the abnormality in heart vessels. This type of diagnosis takes a lot of time by the concerned physician. In our proposed method, we segmented the interested regions of heart vessels and then classified. Segmentation and classification of heart angiography provides significant information for the physician as well as patient. Contradictorily, in the mention domain of heart angiography, the charge is prone to error, phase overwhelming, and thought-provoking task for the physician (heart specialist). An automatic segmentation and classification of heart blood vessels descriptions can improve the truthfulness and speed up the finding of heart illnesses. In this work, we recommend a computer-assisted conclusion arrangement for the localization of human heart blood vessels within heart angiographic imageries by using multiclass ensemble classification mechanism. In the proposed work, the heart blood vessels will be first segmented, and the various features according to accuracy have been extracted. Low-level features such as texture, statistical, and geometrical features were extracted in human heart blood vessels. At last, in the proposed framework, heart blood vessels have been categorized in their four respective classes including normal, block, narrow, and blood flow-reduced vessels. The proposed approach has achieved best result which provides very useful, easy, accurate, and time-saving environment to cardiologists for the diagnosis of heart-related diseases.

scholarly journals Computerized Reconstruction of Pulpal Blood Vessels Examined under Confocal Microscope

2015 ◽  
Vol 19 (1) ◽  
pp. 43-49
Author(s):  
Anna Digka ◽  
Kleoniki Lyroudia ◽  
Lucie Kubinova ◽  
Georgia Karayannopoulou ◽  
Ioannis Marras ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Cross Sections ◽  
Automatic Segmentation ◽  
Spatial Relationship ◽  
Confocal Microscope ◽  
Confocal Laser ◽  
Computer Assisted ◽  
3 Dimensional ◽  
Dimensional Reconstruction ◽  
And Function

SUMMARYThe purpose of this study was the evaluation of 3 different histological methods for studying pulpal blood vessels in combination with 2 types of confocal microscope and computer assisted 3-dimensional reconstruction. 10 human, healthy, free of restorations or caries teeth that were extracted for orthodontic reasons were used. From these teeth, the pulp tissues of 5 were removed, fixed in formalin solution, dehydrated and embedded in paraffin. Serial cross sections 5μm thick were taken from 3 of the above mentioned pulpal tissues and stained with CD34 according to the immunohistochemical ABC technique, while the rest 2 were stained with CD34 and Cy5 by means of immunofluorescence after serial cross sectioning of 10μm. 5 of the 10 teeth were fixed, decalcified, serial cross sectioned (30μm thickness) and stained with eosin. The physical sections were examined under 2 types of confocal laser microscope. Serial images were taken for each section, alignment of the images was followed and finally 3-dimensional reconstructions of the pulpal vessels were achieved.The combined use of immunofluorescence, confocal microscope and automatic segmentation proved to be a useful method for the detailed study of pulpal vasculature. The above method provides deep knowledge of the form and spatial relationship even of the smallest pulpal blood vessels with neighbouring structures like odontoblasts, which are essential for the fully understanding of their role and function within the dental pulp.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

scholarly journals Classification of multiwavelength transients with Machine Learning

2020 ◽  
Author(s):  
K Sooknunan ◽  
M Lochner ◽  
Bruce A Bassett ◽  
H V Peiris ◽  
R Fender ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Sample ◽  
Light Curves ◽  
Machine Learning Techniques ◽  
Optical Data ◽  
Test Time ◽  
Test Accuracy ◽  
Training Set ◽  
The Impact

Abstract With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the eleven classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78%. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97%, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19%.

Computer-assisted data processing of images of mucosal and submucosal blood vessels of the stomach obtained by visible and infrared endoscopy using a directional-contrast filter

1994 ◽  
Vol 40 (5) ◽  
pp. 621-628 ◽  
Author(s):  
Hidetoshi Ohta ◽  
Yutaka Kohgo ◽  
Yasuo Takahashi ◽  
Ryuzou Koyama ◽  
Hideo Suzuki ◽  
...  
Keyword(s):  
Data Processing ◽  
Blood Vessels ◽  
Computer Assisted
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