scholarly journals An Automated Detection and Classification of Cataract and Glaucoma Using RCNN

2021 ◽  
Vol 9 (VI) ◽  
pp. 4959-4967
Author(s):  
Alida E T
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Automated Detection ◽  
Classification Approach ◽  
Glaucoma Screening ◽  
Glaucoma Detection ◽  
Computer Aided Analysis ◽  
The World ◽  
Computer Aided

one of the human’s deterioration is visual impairment. Cataract and Glaucoma are the most prevailing cause blindness in the world. Early detection and treatment is the best way to prevent the blindness. Currently grading is done by human graders, it is found to be time taking and grading is usually subjective. Computer aided analysis can help human graders. An automated cataract and glaucoma detection and classification approach is proposed in this paper, to grade more objectively. Region based convolution neural network (RCNN) is used to classification process. The percentage of accuracy of classification obtained for cataract and glaucoma is 98.9% and 97.8% respectively. The method is especially suitable for cataract and glaucoma screening in the underdeveloped areas or areas which are in shortage of ophthalmic resources. It can also improve the accessibility of ophthalmic medical treatment.

scholarly journals Ensemble learning based classifier to predict depression caused due to pandemic

2021 ◽  
Vol 2089 (1) ◽  
pp. 012026
Author(s):  
P Vaishali ◽  
P L S Kumari
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Text Classification ◽  
Virus Disease ◽  
Model Accuracy ◽  
Research Article ◽  
The World ◽  
Social Gathering ◽  
Second Wave

Abstract Pandemic caused due to Corona Virus Disease 2019 (COVID-19) affected each and every person life throughout the world. First wave of COVID-19 followed by second wave made situation more panic. Government declared Lockdown imposed strict prohibition on social gathering, unnecessary outing, travelling, and education. During home quarantine, people shared opinion, expressed views, feelings on social media. Home isolation and quarantine affected mental health of people which may lead to depression. Hence in this research article depression is predicted by implementing Neural Network based model. At first level this model implements text classification of COVID-19 based Tweets. Neural network model accuracy is 86.85%. In next level, using same tweet dataset as input, Ensemble learning based model is constructed. This model uses one of the boosting techniques known as Adaboost. Model is executed by varying Train-test-validation ratio. It is observed that accuracy of the model is improved. The model showed accuracy of 99.33 % successfully in every execution. Obtained results are compared with previous work in same area.

Bio-Medical Image Processing

2020 ◽  
pp. 158-169
Author(s):  
Rasmita Lenka ◽  
Koustav Dutta ◽  
Ashimananda Khandual ◽  
Soumya Ranjan Nayak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Early Detection ◽  
Digital Image Processing ◽  
Medical Science ◽  
Medical Image Processing ◽  
The World ◽  
Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

scholarly journals Computational classification of animals for a highway detection system

2021 ◽  
Vol 58 ◽  
pp. e174951
Author(s):  
Denis Sato ◽  
Adroaldo José Zanella ◽  
Ernane Xavier Costa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Road Safety ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

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