scholarly journals Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Narut Butploy ◽  
Wanida Kanarkard ◽  
Pewpan Maleewong Intapan
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Computer Technology ◽  
World Population ◽  
Learning Approach ◽  
Human Beings ◽  
Prototype Tool ◽  
Proposed Model ◽  
Low Sensitivity

A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides’s egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg.

A deep learning approach for fast detection and classification of concrete damage

2021 ◽  
Vol 128 ◽  
pp. 103785
Author(s):  
Yongqing Jiang ◽  
Dandan Pang ◽  
Chengdong Li
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Fast Detection ◽  
Concrete Damage

scholarly journals A Deep Learning Approach for the Classification of Neuronal Cell Types

2018 ◽  
Author(s):  
Alessio P. Buccino ◽  
Torbjorn V. Ness ◽  
Gaute T. Einevoll ◽  
Gert Cauwenberghs ◽  
Philipp D. Hafliger
Keyword(s):  
Deep Learning ◽  
Neuronal Cell ◽  
Cell Types ◽  
Learning Approach

scholarly journals Two-Stage Deep Learning Approach to the Classification of Fine-Art Paintings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 41770-41781 ◽  
Author(s):  
Catherine Sandoval ◽  
Elena Pirogova ◽  
Margaret Lech
Keyword(s):  
Deep Learning ◽  
Fine Art ◽  
Learning Approach ◽  
Two Stage

scholarly journals Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

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