Comparative Study of Machine Learning with Novel Feature Extraction and Transfer Learning to Perform Detection of Glaucoma in Fundus Retinal Images

Diabetic retinopathy is proved to be one of the most important eye disorders in recent decades that late diagnosis of it may cause low vision or even blindness. Specialist are able to detect retinopathy in retinal images using machine learning as a decision support system which helps accelerate and facilitate the diagnosis. The automated diabetic retinopathy is a difficult computer vision problem –with the goal of detecting features of retinopathy. The present chapter is written with the purpose of analyzing and comparing different feature extraction methods to evaluate the best algorithm for detection retinopathy with least error. Extracted features using these methods are used to classify images into normal and altered groups.

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Review on anomalous gait behavior detection using machine learning algorithms

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i5.2255 ◽

2020 ◽

Vol 9 (5) ◽

pp. 2090-2096

Author(s):

Hana’ Abd Razak ◽

M. Ahmed M. Saleh ◽

Nooritawati Md Tahir

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Deep Learning ◽

Transfer Learning ◽

Gait Recognition ◽

Learning Algorithms ◽

Anomalous Behavior ◽

Machine Learning Algorithms ◽

Behavior Detection ◽

Detection And Recognition

A review on anomalous behavior in crime by other researchers is discussed in this study that focused specifically on the linkage between anomalous behaviors. Next, comprehensive reviews related to gait recognition in utilizing machine learning algorithms for detection and recognition of anomalous behavior is elaborated too. The review begins with the conventional approach of gait recognition that includes feature extraction and classification using PCA, OLS, ANN, and SVM. Further, the review focused on utilization of deep learning namely CNN for anomalous gait behavior detection and transfer learning using pre-trained CNNs such as AlexNet, VGG, and a few more. To the extent of our knowledge, very few studies investigated and explored crime related anomalous behavior based on their gaits, hence this will be the next study that we will explore.

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Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

Mathematical Problems in Engineering ◽

10.1155/2018/6290498 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 8

Author(s):

Nhat-Duc Hoang ◽

Quoc-Lam Nguyen

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Extraction ◽

Comparative Study ◽

Asphalt Pavement ◽

Classification Performance ◽

Support Vector ◽

Learning Approaches ◽

Pavement Condition

Periodic surveys of asphalt pavement condition are very crucial in road maintenance. This work carries out a comparative study on the performance of machine learning approaches used for automatic pavement crack recognition. Six machine learning approaches, Naïve Bayesian Classifier (NBC), Classification Tree (CT), Backpropagation Artificial Neural Network (BPANN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), and Least Squares Support Vector Machine (LSSVM), have been employed. Additionally, Median Filter (MF), Steerable Filter (SF), and Projective Integral (PI) have been used to extract useful features from pavement images. In the feature extraction phase, performance comparison shows that the input pattern including the diagonal PIs enhances the classification performance significantly by creating more informative features. A simple moving average method is also employed to reduce the size of the feature set with positive effects on the model classification performance. Experimental results point out that LSSVM has achieved the highest classification accuracy rate. Therefore, this machine learning algorithm used with the feature extraction process proposed in this study can be a very promising tool to assist transportation agencies in the task of pavement condition survey.

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A Comparative Study on Diabetic Retinopathy Detection Using Texture-Based Feature Extraction Techniques

Computational Tools and Techniques for Biomedical Signal Processing - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-0660-7.ch012 ◽

2017 ◽

pp. 242-271

Author(s):

Azam Asilian Bidgoli ◽

Hossein Ebrahimpour-Komleh ◽

Seyed Jalaleddin Mousavirad

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Feature Extraction ◽

Diabetic Retinopathy ◽

Decision Support ◽

Support System ◽

Low Vision ◽

Extraction Methods ◽

Retinal Images ◽

Eye Disorders

Diabetic retinopathy is proved to be one of the most important eye disorders in recent decades that late diagnosis of it may cause low vision or even blindness. Specialist are able to detect retinopathy in retinal images using machine learning as a decision support system which helps accelerate and facilitate the diagnosis. The automated diabetic retinopathy is a difficult computer vision problem –with the goal of detecting features of retinopathy. The present chapter is written with the purpose of analyzing and comparing different feature extraction methods to evaluate the best algorithm for detection retinopathy with least error. Extracted features using these methods are used to classify images into normal and altered groups.

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Computer Aid Screening of COVID19 using X-ray and CT Scan Images: A Comparative Study

10.20944/preprints202008.0472.v1 ◽

2020 ◽

Author(s):

Prabira Kumar Sethy ◽

Chanki Pandey ◽

Santi Kumari Behera

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Ct Scan ◽

Transfer Learning ◽

Screening Method ◽

Learning Approaches ◽

X Ray ◽

Computer Aid ◽

Deep Feature ◽

Deep Feature Extraction

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

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Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison

Journal of X-Ray Science and Technology ◽

10.3233/xst-200784 ◽

2021 ◽

pp. 1-14

Author(s):

Prabira Kumar Sethy ◽

Santi Kumari Behera ◽

Komma Anitha ◽

Chanki Pandey ◽

M.R. Khan

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Transfer Learning ◽

Classification Models ◽

X Ray ◽

Computer Aid ◽

Deep Feature ◽

Chest X Ray ◽

Image Set ◽

Deep Feature Extraction

The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases.

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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