Chronic Kidney Disease (CKD) prevalence is going to increase year by year. CKD prediction can be used as one of references for further treatment. The success of CKD prediction usually depend on classifier selected. This paper proposes and evaluates Kernel-based Extreme Learning Machine to predict Chronic Kidney Disease. Subsequently, various kernel-based ELM were evaluated. We compared the performance of four kernels-based ELM, namely RBF-ELM, Linear-ELM, Polynomial-ELM, Wavelet-ELM and the performance of standard ELM. The result showed that radial basis function extrem learning machine (RBF -ELM) was higher than those from the other tested and give the best prediction sensitivity and specificity of 99.38% and 100% respectively

Evaluation of Kernel-Based Extreme Learning Machine Performance for Prediction of Chronic Kidney Disease

Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study.

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Deep Learning Approaches for Automatic Localization in Medical Images

Diabetic retinopathy is the prominent cause of blindness in the working population of the developed world [11]. It is estimated to affect over 314 million people in the next two years. Exudates, micro aneurysms, augmented blood vessels, fluid drip, cotton wool spot, hemorrhages are preliminary signs of diabetic retinopathy. Early detection of these signs will help chances to initiate precautionary measures and thereby avoid permanent damage to the eye sight. In this paper we present systematic approach for feature selection and a machine learning [12] model to classify DR images into five category of varying stage including normal. Dataset from Kaggle is employed for experimentation and extracted around 11 features and for each data 25 features were extracted. 11 among 25 features are selected based on examining low variance. We experimented with various classification models such as Logistic regression model, random forest classifier, Gaussian NB classifier, KNN, Decision tree classifier, Gradient boosting classifier. Results in each phase are validated by 10 fold cross validation method. We compare our results with recently reported literatures. It is found that proposed model presents out performing results with sensitivity equal to 84% for Decision Tree classifier, specificity equal to 82% for Gaussian NB, and accuracy equal to 88% for KNN. The proposed model can be effectively used to aid diagnostic decisions of stage of DR.

Multilevel Classification Model for Diabetic Retinopathy

Proliferative diabetic retinopathy is the advanced stage of diabetic retinopathy (DR) resulting in the growth of abnormal vessels on the retinal surface termed as neovascularization. This article primarily deals with the timely detection and classification of retinal images into healthy, neovascularization on optic disc, and elsewhere using an extreme learning machine (ELM) classifier. Initially, a binary mask is employed to enhance the foreground pixels. The resultant image is processed for the extraction of a retinal vessel map using Hessian‐based Frangi filter. For the classification of retinal images, an optimal feature set of 14 features including seven‐moment invariant‐based features are extracted from the vascular map using sequential recursive feature elimination algorithm. Further, the training of the ELM classifier is carried out using the K‐fold cross‐validation technique to improve the performance of the classifier. The proposed method achieves an average accuracy of 98% and an average error rate of less than 0.005 when tested on different globally accessible datasets. Apart from the different statistical results like sensitivity, specificity, and area under curve are estimated as 98.5%, 100%, and 94.2%, respectively, for validating the algorithm. Comparison results reveal that the ELM classifier outperforms the SVM classifier in terms of accuracy and error rate.

Classification of neovascularization on retinal images using extreme learning machine

International Diabetes Federation (IDF) reports that diabetes is one of the rapidly growing illnesses. About 463 million adults between 20-79 years have diabetes. There are also millions of undiagnosed patients. It is estimated that there will be about 578 million diabetics by 2030 [1]. Diabetes reasons different eye diseases. Diabetic retinopathy (DR) is one of them and is also one of the most common vision loss or blindness worldwide. DR progresses slowly and has few indicators in the early stages. It makes the diagnosis of DR a problematic task. Automated systems promise to support the diagnosis of DR. Many deep learning-based models have been developed for DR classification. This study aims to support ophthalmologists in the diagnosis process and increase the diagnosis performance of DR through a hybrid model. A publicly available Messidor-2 dataset was used in this study, comprised of retinal images. In the proposed model, first, images were pre-processed and a deep learning model, namely, InceptionV3 was used in feature extraction where a transfer learning approach is applied. Next, the number of features in obtained feature vectors was decreased with feature selection by Simulated Annealing (SA). Lastly, the best representation features were used in XGBoost model. The XGBoost algorithm gives an accuracy of 92.26% in a binary classification task. This study shows that a pre-trained ConvNet with a metaheuristic algorithm for feature selection gives a satisfactory result in the diagnosis of DR.

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A Hybrid Deep Learning-Metaheuristic Model for Diagnosis of Diabetic Retinopathy

https://www.mdpi.com/2227-9032/10/4/677/pdf?version=1649062257

Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images

A computationally effective method for diagnosing the severity of Diabetic Retinopathy is proposed. The proposed approach follows 3 stages; the preprocessing, feature extraction and classification. The purpose of the first step is to make the image suitable for subsequent process. In feature extraction, number of micro aneurysms and texture features were extracted by morphological process and the last step is the classification, in which the images are categorized by these features with the help of ELM classifier. The above procedure was tested and analyzed using images in DIARETDB0 and DRIVE database and we were able to achieve accuracy of 95% with good training speed.

I. S. Hephzi Punithavathi

Papers

Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images

Severity grading of diabetic retinopathy using extreme learning machine

Optimal dense convolutional network model for image classification in unmanned aerial vehicles based ad hoc networks