An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images

2018 ◽  
Vol 65 (6) ◽  
pp. 1382-1390 ◽  
Author(s):  
Kedir M. Adal ◽  
Peter G. van Etten ◽  
Jose P. Martinez ◽  
Kenneth W. Rouwen ◽  
Koenraad A. Vermeer ◽  
...  
Keyword(s):  
Automated System ◽  
Fundus Images

scholarly journals Diabetic retinopathy detection and classification using capsule networks

2021 ◽  
Author(s):  
G. Kalyani ◽  
B. Janakiramaiah ◽  
A. Karuna ◽  
L. V. Narasimha Prasad
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Good Prognosis ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Specific Class ◽  
Fundus Images ◽  
The People ◽  
Recent Developments

AbstractNowadays, diabetic retinopathy is a prominent reason for blindness among the people who suffer from diabetes. Early and timely detection of this problem is critical for a good prognosis. An automated system for this purpose contains several phases like identification and classification of lesions in fundus images. Machine learning techniques based on manual extraction of features and automatic extraction of features with convolution neural network have been presented for diabetic retinopathy detection. The recent developments like capsule networks in deep learning and their significant success over traditional machine learning methods for a variety of applications inspired the researchers to apply them for diabetic retinopathy diagnosis. In this paper, a reformed capsule network is developed for the detection and classification of diabetic retinopathy. Using the convolution and primary capsule layer, the features are extracted from the fundus images and then using the class capsule layer and softmax layer the probability that the image belongs to a specific class is estimated. The efficiency of the proposed reformed network is validated concerning four performance measures by considering the Messidor dataset. The constructed capsule network attains an accuracy of 97.98%, 97.65%, 97.65%, and 98.64% on the healthy retina, stage 1, stage 2, and stage 3 fundus images.

Automated System Design for Classification of Chronic Lung Viruses using Non-Linear Dynamic System Features and K-Nearest Neighbour

2021 ◽  
Author(s):  
Misha Urooj Khan ◽  
Ayesha Farman ◽  
Asad Ur Rehman ◽  
Nida Israr ◽  
Muhammad Zulqarnain Haider Ali ◽  
...  
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Linear Dynamic System ◽  
Automated System ◽  
Nearest Neighbour ◽  
Linear Dynamic ◽  
Non Linear

scholarly journals THE CORRELATION BETWEEN LEAN MEAT PERCENTAGE IN PRIMAL CUTS AND TOTAL LEAN MEAT PERCENTAGE IN CARCASS

2018 ◽  
Vol 3 (2) ◽  
pp. 33-39
Author(s):  
Andrey V. Pavlov ◽  
Andrey I. Rud ◽  
Maxim A. Zankevich
Keyword(s):  
Muscle Tissue ◽  
Conflict Of Interest ◽  
Correlation Coefficients ◽  
Automated System ◽  
Lean Meat ◽  
Additional Weight ◽  
A Value ◽  
Slaughter Pigs ◽  
Equal Contribution

With the help of the automated system for the classification of carcasses of pigs, AutoFOM ultrasound have been processed 56682 carcasses of slaughter pigs with an average carcass weight of 94.3 kg. The mass and yield of muscle tissue from the main cuts in the carcass is shown. Correlation coefficients between the mass and the content of muscle tissue in the carcass and the main (premium) cuts (ham, neck, shoulder, belly, and loin) were studied. It is shown how the increase in the weight of each of the cuts affects the content of muscle tissue in the carcass and in the cut. For example, it was found that when the weight of the belly is increased by 10 kg (from 6 to 16 kg), the percentage of muscle tissue from carcass is reduced by 3.3% (from 54.5 to 51.8%), which is approximately 0.33% for 1 kg of additional weight of the belly. With an increase in the weight of the loin from 4 to 14 kg, the yield of muscle tissue from the carcass on the contrary increased by 11.6%, i.е. 1.16% for each additional kg of loin weight. A value (in absolute and relative units) of the main cuts is given. The conclusion is made about the prospects of using the obtained data in the creation of a specialized terminal line of pigs, characterized by an increased content of weight of premium cuts in the carcass.ContributionAll authors bear responsibility for the work and presented data. All authors made an equal contribution to the work. The authors were equally involved in writing the manuscript and bear the equal responsibility for plagiarism.Conflict of interestThe authors declare no conflict of interest.

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