Machine Deep Learning Techniques Research Articles

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

Alzheimer Disease Detection Empowered with Transfer Learning

Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.

Energy Demand Forecasting Using Fused Machine Learning Approaches

The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches.

Money Laundering Detection using Machine Learning and Deep Learning

In recent years, money laundering activities have shown rapid progress and have indeed become the main concern for governments and financial institutions all over the world. As per recent statistics, $800 billion to $2 trillion is the estimated value of money laundered annually, in which $5 billion of the total is obtained from cryptocurrency money laundering. As per the financial action task force (FATF), the criminals may trade illegally obtained fiat money for the cryptocurrency. Accordingly, detecting and preventing illegal transactions becomes a serious threat to governments and it has been indeed challenging. To combat money laundering, especially in cryptocurrency, effective techniques for detecting suspicious transactions must be developed since the current preventive efforts are outdated. In fact, deep learning and machine learning techniques may provide novel methods to detect suspect currency movements. This study investigates the applicability of deep learning and machine learning techniques for anti-money laundering in cryptocurrency. The techniques employed in this study are Deep Neural Network (DNN), random forest (RF), K-Nearest Neighbors(KNN), and Naive Bayes (NB) with the bitcoin elliptic dataset. It was observed that the DNN and random forest classifier have achieved the highest accuracy rate with promising findings in decreasing the false positives as compared to the other classifiers. In particular, the random forest classifier outperforms DNN and achieves an F1-score of 0.99%.

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most skin diseases have similar features, which make it challenging for dermatologists to diagnose patients accurately. Therefore, machine and deep learning techniques can have a critical role in diagnosing dermatoscopy images and in the accurate early detection of skin diseases. In this study, systems for the early detection of skin lesions were developed. The performance of the machine learning and deep learning was evaluated on two datasets (e.g., the International Skin Imaging Collaboration (ISIC 2018) and Pedro Hispano (PH2)). First, the proposed system was based on hybrid features that were extracted by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and wavelet transform (DWT). Such features were then integrated into a feature vector and classified using artificial neural network (ANN) and feedforward neural network (FFNN) classifiers. The FFNN and ANN classifiers achieved superior results compared to the other methods. Accuracy rates of 95.24% for diagnosing the ISIC 2018 dataset and 97.91% for diagnosing the PH2 dataset were achieved using the FFNN algorithm. Second, convolutional neural networks (CNNs) (e.g., ResNet-50 and AlexNet models) were applied to diagnose skin diseases using the transfer learning method. It was found that the ResNet-50 model fared better than AlexNet. Accuracy rates of 90% for diagnosing the ISIC 2018 dataset and 95.8% for the PH2 dataset were reached using the ResNet-50 model.

Review on diabetic retinopathy with deep learning methods.

Purpose: The purpose of our review paper is to examine many existing works of literature presenting the different methods utilized for diabetic retinopathy (DR) recognition employing deep learning (DL) and machine learning (ML) techniques, and also to address the difficulties faced in various datasets used by DR. Approach: DR is a progressive illness and may become a reason for vision loss. Early identification of DR lesions is, therefore, helpful and prevents damage to the retina. However, it is a complex job in view of the fact that it is symptomless earlier, and also ophthalmologists have been needed in traditional approaches. Recently, automated identification of DR-based studies has been stated based on image processing, ML, and DL. We analyze the recent literature and provide a comparative study that also includes the limitations of the literature and future work directions. Results: A relative analysis among the databases used, performance metrics employed, and ML and DL techniques adopted recently in DR detection based on various DR features is presented. Conclusion: Our review paper discusses the methods employed in DR detection along with the technical and clinical challenges that are encountered, which is missing in existing reviews, as well as future scopes to assist researchers in the field of retinal imaging.

Diagnosis of Ophthalmic Diseases in Fundus Image by Using Deep Learning

Abstract: In the human eye, Damage in the retina may cause ophthalmic diseases like cataracts, AMD, Hypertensive retinopathy, myopia, etc. To cure these diseases, many ophthalmologists use retinal fundus images as an important information source to find out ophthalmic diseases. Multiple techniques have been introduced for the screening of ocular diseases. Today’s world is in great demand to find out ocular diseases by using deep learning and machine learning techniques. This paper uses pre-trained deep neural networks to determine five categories of ophthalmic diseases such as cataract, AMD, Hypertensive retinopathy, myopia, and normal. Dataset is created into binary and multiclass, then trained on Resnet-101 of convolutional neural network (CNN) and evaluated. The accuracy of this model is found to be 90.38% and 88.5% for binary and multiclass respectively. Keywords: Retinal fundus image, Ocular diseases, CNN, ResNet, deep learning. Image processing, Ensemble classifier

Prediction framework for upper body sedentary working behaviour by using deep learning and machine learning techniques.

Public health experts and healthcare professionals are gradually identifying sedentary activity as a population-wide, pervasive health risk. The purpose of this paper is to propose a method to identify the changes in posture during sedentary work and to give feedback by analysing the identified posture of the upper body, i.e. hands, shoulder, and head positioning. After capturing the image of the human pose, pre-processing of the image takes place with a bandpass filter, which helps to reduce the noise and morphological operation, which is used to carry out the process of dilation, erosion and opening of an image. To predict the results easily with the use of texture feature extraction, it helps to extract the image’s feature. Then, accuracy is predicted by using the deep neural network techniques, to predict the result accurately. After prediction and analysis, the feedback system is developed to alert individuals through the alarm system. The proposed method is formulated by using DNN for prediction in the MATLAB software tool. The results show accuracy, sensitivity and specificity of the prediction using a deep neural network are 97.2%, 88.7% and 99.1%. The proposed method is compared with the existing methods SVM, Random Forest and KNN algorithms. The accuracy, sensitivity and specificity of the existing algorithms are SVM with 77.6%, 57.4 and 97.8%; Random Forest with 80.6%, 63.7% and 97.5%; and KNN with 65.8%, 61.2%, and 95.1%. This concept helps to prevent the impact of sedentary activity on fatal and non-fatal cardiovascular and musculoskeletal diseases, respectively.

An intrusion detection system for health-care system using machine and deep learning

PurposeOne of the important key components of health care–based system is a reliable intrusion detection system. Traditional techniques are not adequate to handle complex data. Also, the diversified intrusion techniques cannot meet current network requirements. Not only the data is getting increased but also the attacks are increasing very rapidly. Deep learning and machine learning techniques are very trending in the area of research in the area of network security. A lot of work has been done in this area by still evolutionary algorithms along with machine learning is very rarely explored. The purpose of this study is to provide novel deep learning framework for the detection of attacks.Design/methodology/approachIn this paper, novel deep learning is the framework is proposed for the detection of attacks. Also, a comparison of machine learning and deep learning algorithms is provided.FindingsThe obtained results are more than 99% for both the data sets.Research limitations/implicationsThe diversified intrusion techniques cannot meet current network requirements.Practical implicationsThe data is getting increased but also the attacks are increasing very rapidly.Social implicationsDeep learning and machine learning techniques are very trending in the area of research in the area of network security.Originality/valueNovel deep learning is the framework is proposed for the detection of attacks.

Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification

BackgroundAlthough biopsy is the gold standard for tumour grading, being invasive, this procedure also proves fatal to the brain. Thus, non-invasive methods for brain tumour grading are urgently needed. Here, a magnetic resonance imaging (MRI)-based non-invasive brain tumour grading method has been proposed using deep learning (DL) and machine learning (ML) techniques. MethodFour clinically applicable datasets were designed. The four datasets were trained and tested on five DL-based models (convolutional neural networks), AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, and five ML-based models, Support Vector Machine, K-Nearest Neighbours, Naïve Bayes, Decision Tree, and Linear Discrimination using five-fold cross-validation. A majority voting (MajVot)-based ensemble algorithm has been proposed to optimise the overall classification performance of five DL and five ML-based models. ResultsThe average accuracy improvement of four datasets using the DL-based MajVot algorithm against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 models was 2.02%, 1.11%, 1.04%, 2.67%, and 1.65%, respectively. Further, a 10.12% improvement was seen in the average accuracy of four datasets using the DL method against ML. Furthermore, the proposed DL-based MajVot algorithm was validated on synthetic face data and improved the male versus female face image classification accuracy by 2.88%, 0.71%, 1.90%, 2.24%, and 0.35% against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, respectively. ConclusionThe proposed MajVot algorithm achieved promising results for brain tumour classification and is able to utilise the combined potential of multiple models.

Estimation of Energy Produced in Hydroelectric Power Plant Industrial Automation Using Deep Learning and Hybrid Machine Learning Techniques

Today, due to the rapidly increasing environmental pollution, the importance of energy obtained from renewable energy sources is increasing. One of these important renewable energy sources is Hydroelectric Power Plants. Using hybrid-machine learning algorithms, non-hybrid machine learning algorithms and deep learning models, the electrical energy produced by Hydroelectric Power Plant was estimated based on seventeen different input parameters such as flow rate and generator current. In this study, hyperparameters were tuned and feature selection was made with a Genetic Algorithm in order to increase the accuracy rate in artificial intelligence models. In addition, all artificial intelligence models were evaluated according to the processing time, Coefficient of determination, Mean Squared Error, Mean Absolute Error and Root Mean Square Error performance evaluation criteria. It was seen that the electrical energy production value was determined with the Random Forest Algorithm, which is one of the non-hybrid machine learning algorithms, with an accuracy rate of 99.641%, with Genetic Algorithm + Random Forest Algorithm, which is a hybrid machine learning algorithm, with an accuracy rate of 99.672% and with Deep Neural Network, which is one of the deep learning models, with a 99.99% accuracy rate. According to the results, it was determined that the Deep Neural Networks model determined the electrical energy production value with a higher accuracy rate compared to hybrid and non-hybrid machine learning algorithms.

Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings.

Background:Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are among the leading causes of blindness in the United States and other developed countries. Early detection is the key to prevention and effective treatment. We have built an artificial intelligence-based screening system which utilizes a cloud-based platform for combined large scale screening through primary care settings for early diagnosis of these diseases.Methods:iHealthScreen Inc., an independent medical software company, has developed automated AMD and DR screening systems utilizing a telemedicine platform based on deep machine learning techniques. For both diseases, we prospectively imaged both eyes of 340 unselected non-dilated subjects over 50 years of age. For DR specifically, 152 diabetic patients at New York Eye and Ear faculty retina practices, ophthalmic and primary care clinics in New York city with color fundus cameras. Following the initial review of the images, 308 images with other confounding conditions like high myopia and vascular occlusion, and poor quality were excluded, leaving 676 eligible images for AMD and DR evaluation. Three ophthalmologists evaluated each of the images, and after adjudication, the patients were determined referrable or non-referable for AMD DR. Concerning AMD, 172 were labeled referable (intermediate or late), and 504 were non-referable (no or early). Concurrently, regarding DR, 33 were referable (moderate or worse), and 643 were non-referable (none or mild). All images were uploaded to iHealthScreen’s telemedicine platform and analyzed by the automated systems for both diseases. The system performances are tested on per eye basis with sensitivity, specificity, accuracy, and kappa scores with respect to the professional graders.Results:In identifying referable DR, the system achieved a sensitivity of 97.0% and a specificity of 96.3%, and a kappa score of 0.70 on this prospective dataset. For AMD, the sensitivity was 86.6%, the specificity of 92.1%, and a kappa score of 0.76.Conclusions:The AMD and DR screening tools achieved excellent performance operating together to identify two retinal diseases prospectively in mixed datasets, demonstrating the feasibility of such tools in the early diagnosis of eye diseases. These early screening tools will help create an even more comprehensive system capable of being trained on other retinal pathologies, a goal within reach for public health deployment.

Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction

BackgroundDiabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes.MethodsBased on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models.ResultsThe performance measures such as precision, accuracy, recall, F1 score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively.ConclusionThe proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.

Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

Modeling urban evapotranspiration using remote sensing, flux footprints, and artificial intelligence

As climate change progresses, urban areas are increasingly affected by water scarcity and the urban heat island effect. Evapotranspiration (ET) is a crucial component of urban greening initiatives of cities worldwide aimed at mitigating these issues. However, ET estimation methods in urban areas have so far been limited. An expanding number of flux towers in urban environments provide the opportunity to directly measure ET by the eddy covariance method. In this study, we present a novel approach to model urban ET by combining flux footprint modeling, remote sensing and geographic information system (GIS) data, and deep learning and machine learning techniques. This approach facilitates spatio-temporal extrapolation of ET at a half-hourly resolution; we tested this approach with a two-year dataset from two flux towers in Berlin, Germany. The benefit of integrating remote sensing and GIS data into models was investigated by testing four predictor scenarios. Two algorithms (1D convolutional neural networks (CNNs) and random forest (RF)) were compared. The best-performing models were then used to model ET values for the year 2019. The inclusion of GIS data extracted using flux footprints enhanced the predictive accuracy of models, particularly when meteorological data was more limited. The best-performing scenario (meteorological and GIS data) showed an RMSE of 0.0239 mm/h and R2 of 0.840 with RF and an RMSE of 0.0250 mm/h and a R2 of 0.824 with 1D CNN for the more vegetated site. The 2019 ET sum was substantially higher at the site surrounded by more urban greenery (366 mm) than at the inner-city site (223 mm), demonstrating the substantial influence of vegetation on the urban water cycle. The proposed method is highly promising for modeling ET in a heterogeneous urban environment and can support climate change mitigation initiatives of urban areas worldwide.

COLLAPSE WARNING SYSTEM USING LSTM NEURAL NETWORKS FOR CONSTRUCTION DISASTER PREVENTION IN EXTREME WIND WEATHER

Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether the scaffold structure could withstand the stress of the wind force. Conceptually, the scaffolds collapsed by the warning system developed in the study contains three modules. The first module involves the establishment of wind velocity prediction models. This study employed various deep learning and machine learning techniques, namely deep neural networks, long short-term memory neural networks, support vector regressions, random forest, and k-nearest neighbors. Then, the second module contains the analysis of wind force on the scaffolds. The third module involves the development of the scaffold collapse evaluation approach. The study area was Taichung City, Taiwan. This study collected meteorological data from the ground stations from 2012 to 2019. Results revealed that the system successfully predicted the possible collapse time for scaffolds within 1 to 6 h, and effectively issued a warning time. Overall, the warning system can provide practical warning information related to the destruction of scaffolds to construction teams in need of the information to reduce the damage risk.

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

More and more individuals are now using online social networks and resources throughout this day and age to not only interact and to communicate but also for sharing their views, experiences, ideas, impression about anything. The analysis of sentiments is the identification and categorization of these views to evaluate public opinions on a specific subject, question, product, etc. Day by day, the relevance of sentiment analysis is growing up. Machine learning is an area or field of computer science where, without being specifically programmed, computers can learn. Deep learning is the part of machine learning and deals with the algorithm, which is most widely used as Neural network, neural belief, etc., in which neuronal implementations are considered. For sentiment analysis, it compares their performance and accuracy so then it can be inferred that deep learning techniques in most of the cases provide better results. The gap in the precision of these two approaches, however, is not as important enough in certain situations, and so it is best to apply and use the machine learning approaches and methods because these are simpler in terms of Implementation

Deep learning and machine learning techniques to improve hand movement classification in myoelectric control system

A robust myoelectric control system (MCS) is essential for the design of electromyography (EMG) based human–machine interface (HMI) designs such as prosthetics, exoskeleton, wheelchair and humanoid robots. The functionality of the current pattern recognition (PR) technique in MCS is limited by factors such as variation in the user’s limb position. To overcome the effect of this dynamic variation, an invariant higher order statistics – frequency domain feature set (HOS-FD) is proposed in this paper. EMG data from eight hand movements in five limb positions are considered. When trained with three limb position data, the HOS-FD with three hidden layers deep neural network (DNN) achieved a significantly high classification accuracy of 97.84%±0.22 compared to other classifiers viz., single layer artificial neural network (ANN), linear discriminant analysis (LDA), support vector machine (SVM), k nearest neighbor (kNN), decision tree (DT) and Naïve Bayes (NB) classifiers with accuracies of 94.11%±1.63, 95.02%±1.89, 94.63%±2.33, 90.05 ± 4.11, 86.66 ± 4.72 and 78.78%±5.02 respectively. Further, when trained with data from all five limb positions, the proposed feature set with DNN had an accuracy of 99.16%±0.14. The statistical significance of the high classification accuracy obtained using the proposed feature set is also proven using multiple analysis of variance tests (p < 0.001). These results indicate that the proposed method is a promising technique for HMI.

The unknown knowns: a graph-based approach for temporal COVID-19 literature mining

PurposeThe COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research.Design/methodology/approachThe authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered.FindingsFindings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions.Originality/valueTo the authors’ knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.

Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images

Recently, there has been an advancement in the development of innovative computer-aided techniques for the segmentation and classification of retinal vessels, the application of which is predominant in clinical applications. Consequently, this study aims to provide a detailed overview of the techniques available for segmentation and classification of retinal vessels. Initially, retinal fundus photography and retinal image patterns are briefly introduced. Then, an introduction to the pre-processing operations and advanced methods of identifying retinal vessels is deliberated. In addition, a discussion on the validation stage and assessment of the outcomes of retinal vessels segmentation is presented. In this paper, the proposed methods of classifying arteries and veins in fundus images are extensively reviewed, which are categorized into automatic and semi-automatic categories. There are some challenges associated with the classification of vessels in images of the retinal fundus, which include the low contrast accompanying the fundus image and the inhomogeneity of the background lighting. The inhomogeneity occurs as a result of the process of imaging, whereas the low contrast which accompanies the image is caused by the variation between the background and the contrast of the various blood vessels. This means that the contrast of thicker vessels is higher than those that are thinner. Another challenge is related to the color changes that occur in the retina from different subjects, which are rooted in biological features. Most of the techniques used for the classification of the retinal vessels are based on geometric and visual characteristics that set the veins apart from the arteries. In this study, different major contributions are summarized as review studies that adopted deep learning approaches and machine learning techniques to address each of the limitations and problems in retinal blood vessel segmentation and classification techniques. We also review the current challenges, knowledge gaps and open issues, limitations and problems in retinal blood vessel segmentation and classification techniques.