Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
Automated fundus image quality assessment and segmentation of optic disc usin...IJECEIAES
An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIMONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
Automated fundus image quality assessment and segmentation of optic disc usin...IJECEIAES
An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIMONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
Detection and classification of brain tumor are very important because it provides anatomical information of normal and abnormal tissues which helps in early treatment planning and patient's case follow-up. There is a number of techniques for medical image classification. We used PNN (Probabilistic Neural Network Algorithm) for image classification technique based on Genetic Algorithm (GA) and K-Nearest Neighbor (K-NN) classifier for feature selection is proposed in this paper. The searching capabilities of genetic algorithms are explored for appropriate selection of features from input data and to obtain an optimal classification. The method is implemented to classify and label brain MRI images into seven tumor types. A number of texture features (Gray Level Co-occurrence Matrix (GLCM)) can be extracted from an image, so choosing the best features to avoid poor generalization and over specialization is of paramount importance then the classification of the image and compare results based on the PNN algorithm.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
A robust technique of brain mri classification using color features and k nea...Salam Shah
The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three staged model having pre-processing, feature extraction and classification steps. In preprocessing the noise has been removed from grayscale images using a median filter, and then grayscale images have been converted to color (RGB) images. In feature extraction, red, green and blue channels from each channel of the RGB has been extracted because they are so much informative and easier to process. The first three color moments mean, variance, and skewness are calculated for each red, green and blue channel of images. The features extracted in the feature extraction stage are classified into normal and abnormal with K-Nearest Neighbors (k-NN). This method is applied to 100 images (70 normal, 30 abnormal). The proposed method gives 98.00% training and 95.00% test accuracy with datasets of normal images and 100% training and 90.00% test accuracy with abnormal images. The average computation time for each image was .06s.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Retinal image analysis using morphological process and clustering techniquesipij
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
Detection and classification of brain tumor are very important because it provides anatomical information of normal and abnormal tissues which helps in early treatment planning and patient's case follow-up. There is a number of techniques for medical image classification. We used PNN (Probabilistic Neural Network Algorithm) for image classification technique based on Genetic Algorithm (GA) and K-Nearest Neighbor (K-NN) classifier for feature selection is proposed in this paper. The searching capabilities of genetic algorithms are explored for appropriate selection of features from input data and to obtain an optimal classification. The method is implemented to classify and label brain MRI images into seven tumor types. A number of texture features (Gray Level Co-occurrence Matrix (GLCM)) can be extracted from an image, so choosing the best features to avoid poor generalization and over specialization is of paramount importance then the classification of the image and compare results based on the PNN algorithm.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
A robust technique of brain mri classification using color features and k nea...Salam Shah
The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three staged model having pre-processing, feature extraction and classification steps. In preprocessing the noise has been removed from grayscale images using a median filter, and then grayscale images have been converted to color (RGB) images. In feature extraction, red, green and blue channels from each channel of the RGB has been extracted because they are so much informative and easier to process. The first three color moments mean, variance, and skewness are calculated for each red, green and blue channel of images. The features extracted in the feature extraction stage are classified into normal and abnormal with K-Nearest Neighbors (k-NN). This method is applied to 100 images (70 normal, 30 abnormal). The proposed method gives 98.00% training and 95.00% test accuracy with datasets of normal images and 100% training and 90.00% test accuracy with abnormal images. The average computation time for each image was .06s.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Retinal image analysis using morphological process and clustering techniquesipij
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
An effective deep learning network for detecting and classifying glaucomatous...IJECEIAES
Glaucoma is a well-known complex disease of the optic nerve that gradually damages eyesight due to the increase of intraocular pressure inside the eyes. Among two types of glaucoma, open-angle glaucoma is mostly happened by high intraocular pressure and can damage the eyes temporarily or sometimes permanently, another one is angle-closure glaucoma. Therefore, being diagnosed in the early stage is necessary to safe our vision. There are several ways to detect glaucomatous eyes like tonometry, perimetry, and gonioscopy but require time and expertise. Using deep learning approaches could be a better solution. This study focused on the recognition of open-angle affected eyes from the fundus images using deep learning techniques. The study evolved by applying VGG16, VGG19, and ResNet50 deep neural network architectures for classifying glaucoma positive and negative eyes. The experiment was executed on a public dataset collected from Kaggle; however, every model performed better after augmenting the dataset, and the accuracy was between 93% and 97.56%. Among the three models, VGG19 achieved the highest accuracy at 97.56%.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Social media marketing (SMM) is a form of digital marketing that utilizes social media platforms to promote products, services, or brands. The goal of social media marketing is to connect with the target audience, build brand awareness, increase website traffic, and drive engagement and conversions. Here are some key aspects of social media marketing:
Strategy Development:
Identify your target audience: Understand the demographics, interests, and online behavior of your target audience.
Set clear goals: Define specific, measurable, achievable, relevant, and time-bound (SMART) goals for your social media campaigns.
Choose the right platforms: Select social media platforms that align with your target audience and business objectives.
Content Creation:
Create engaging content: Develop content that resonates with your audience, such as images, videos, infographics, and text posts.
Maintain consistency: Establish a consistent posting schedule to keep your audience engaged and informed.
Use a variety of content types: Experiment with different content formats to keep your social media presence diverse and interesting.
Audience Engagement:
Respond to comments and messages: Engage with your audience by responding to comments, messages, and mentions in a timely manner.
Encourage user-generated content: Encourage your followers to create and share content related to your brand.
Run contests and giveaways: Organize contests or giveaways to boost engagement and attract new followers.
Paid Advertising:
Utilize paid social media advertising: Platforms like Facebook, Instagram, Twitter, and LinkedIn offer advertising options to reach a larger audience.
Targeted advertising: Use advanced targeting options to reach specific demographics, interests, and behaviors.
Analytics and Monitoring:
Use analytics tools: Monitor the performance of your social media campaigns using analytics tools provided by the platforms or third-party tools.
Adjust strategies based on data: Analyze the data and adjust your strategies to optimize performance and achieve better results.
Influencer Marketing:
Collaborate with influencers: Partner with influencers who align with your brand to reach a wider audience and build credibility.
Leverage user trust: Influencers can help establish trust with their followers, leading to increased brand credibility.
Social Media Trends:
Stay updated: Keep track of emerging trends in social media marketing and adapt your strategies accordingly.
Experiment with new features: Platforms regularly introduce new features; experiment with these features to stay ahead of the curve.
Remember that effective social media marketing requires a consistent and strategic approach. Regularly assess your performance, listen to your audience, and adjust your strategies to meet your goals.
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.
Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to
screen abnormalities through retinal fundus images by clinicians. However, limited number of
well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a
big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy
severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images
in the training set, we also automatically segment the abnormal patches with an occlusion test,
and model the transformations and deterioration process of DR. Our results can be widely used
for fast diagnosis of DR, medical education and public-level healthcare propagation.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
Fundus Image Classification Using Two Dimensional Linear Discriminant Analysi...INFOGAIN PUBLICATION
It is constructed in this study a classification system of diabetic retinopathy fundus image. The system consists of two phases: training and testing. Each stage consists of preprocessing, segmentation, feature extraction and classification. The tested image comes from the MESSIDOR dataset which has a total of 100 images. The number of classes to be classified consists of four classes with each class consists of 25 images. The classes are normal, mild, moderate and severe of Diabetic retinopathy. In this study, the level of preprocessing uses grayscales green channel, Wavelet Haar, Gaussian filter and Contrast Limited Adaptive Histogram Equalization. The level of segmentation uses masking as a process of doing the subtracting operation of between the original image and the masking image. The purpose of the masking is to split between the object and the background. The feature extraction uses Two Dimensional Linear Discriminant Analysis (2DLDA). The classification uses Support Vector Machine (SVM). The test results of some scenarios show that the highest percentage of accuration of the system is up to 90%.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
An Efficient Integrated Approach for the Detection of Exudates and Diabetic M...acijjournal
Diabetic Retinopathy (DR) is a major cause of blindness. Exudates are one of the primary signs of diabetic retinopathy which is a main cause of blindness that could be prevented with an early screening process In this approach, the process and knowledge of digital image processing to diagnose exudates
from images of retina is applied. An automated method to detect and localize the presence of exudates and Maculopathy from low-contrast digital images of Retinopathy patient’s with non-dilated pupils is proposed. First, the image is segmented using colour K-means Clustering algorithm. The segmented image along with Optic Disc (OD) is chosen. To Classify these segmented region, features based on colour and texture are extracted. The selected feature vector are then classified into exudates and nonexudates using a Support Vector Machine (SVM) Classifier. Also the detection of Diabetic Maculopathy,
which is the severe stage of Diabetic Retinopathy is performed using Morphological Operation. Using a clinical reference standard, images with exudates were detected with 96% success rate. This method appears promising as it can detect the very small areas of exudates.
An Automated Systems for the Detection of Macular Ischemia based-Diabetic Ret...iosrjce
The proposed methodology in this paper marks out application for automatic detection of eye
diseases called Macular Ischemia using image processing techniques. In semi urban and rural areas large
percentages of people suffer from various eye diseases. For diagnoses of various eye diseases, Image processing
technique is used. . Diseases occur in Macula from retinal images have a huge type of textures, shapes and at
times they are difficult to be recognised and identified by doctors. Thus we are trying to optimize and develop
such system which is based on smart image recognition/classification algorithms. This proposed system
provides accuracy, uniformity and speed in performance and a high credence coefficient in results interpreting.
Keywords: Macular Ischemia, diagnosis, textures, consistence
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CLASSIFICATION OF OCT IMAGES FOR DETECTING DIABETIC RETINOPATHY DISEASE USING MACHINE LEARNING
1. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
DOI: 10.5121/sipij.2021.12602 27
CLASSIFICATION OF OCT IMAGES FOR DETECTING
DIABETIC RETINOPATHY DISEASE USING
MACHINE LEARNING
Marwan Aldahami and Umar Alqasemi
Dept. of Electrical and Computer Engineering, King Abdulaziz University, P.O.Box
80200, Jeddah 21589, Saudi Arabia
ABSTRACT
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
KEYWORDS
Image classification, diabetic retinopathy, support vector machine, optical coherence tomography, retina,
machine learning.
1. INTRODUCTION
Diabetic Retinopathy (DR) is a microvascular disease that affects retinal vessels. It is considered
one of the most common diseases that causes vision loss for diabetic patients. In the year 2000,
about 4 million DR cases were estimated in the United States. In 2010, the number of cases was
7.7 million, which is expected to be 14.6 million by 2050. The National Health and Nutrition
Examination Survey (NHANES) conducted a visual analysis in 2005-2008. NHANES estimated
that 29% of people aged 40 years or more had simultaneous DR. Additionally, it has been
estimated that the prevalence of DR in the sample measured was 4% [1]. NHANES also declared
that DR prevalence is approximately 32% in males, while it is approximately 26% in females and
39% in non-Hispanic black people versus 25% in non-Hispanic white people [1].
Optical Coherence Tomography (OCT) is an emerging technology that allows for the study of
blood flow within the eye’s vascular structure [2]. OCT is a non-invasive technique that uses a
low-coherence light to produce high-resolution, cross-sectional, and micrometer-scale images.
The principle of OCT is based on optics theory by measuring the transmitted and reflected optical
signals that contain time-of-flight information, which yields spatial information about scanned
tissue [3].
2. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
28
The early stage of DR is known as non-proliferative diabetic retinopathy (NPDR). During this
stage, the retina vasculature begins a process of change wherein vascular permeability and
capillary occlusion increase. The advanced stage of DR is called proliferative diabetic retinopathy
(PDR), in which vitreous hemorrhage is present. During this stage, the abnormal vessels
commence bleeding into the vitreous humor and may result in tractional retinal detachment in the
patient’s eye, which causes vision impairment. By obtaining OCT angiography images,
ophthalmologists are able to detect diabetic eyes that have a potential risk of developing
retinopathy [4]. Developing an OCT computer-aided detection (CADe) software will assist
ophthalmologists in diagnosing patients in an accurate, fast, and safe manner that will protect
diabetic eyes from vision loss at early stages.
In this paper, we have designed an automated system to classify the OCT into normal images and
images with DR. The system was trained by inserting 160 images with their class and tested by
inserting 54 images. Two types of classifiers were used: Support Vector Machine (SVM) and k-
Nearest Neighbor (kNN).
2. LITERATURE REVIEW
Recent studies have addressed the automated classification of OCT images by extracting the
images’ features and using algorithms for classification or by segmentation. Priya et al. [5]
proposed a system to diagnose diabetic retinopathy disease by using 350 fundus images. The used
images were collected from “Aravind Eye Hospital and Postgraduate Institute of
Ophthalmology”. The images were produced from the fundus camera in RBG form. The authors
started by preprocessing the images to make them suitable for the machine learning system. The
images were converted into gray-scale images. Then, in order to enhance the images’ contrast,
they were subjected to adaptive histogram equalization. After that, the Matched Filter Response
(MFR) and Discrete Wavelet Transform (DWT) were applied to reduce the noise and the images’
size. The authors extracted some features from the images subjected to their study, such as the
blood vessels, NPDR hemorrhage, and PDR exudates, by using image segmentation. They
applied three classifiers: Probabilistic Neural Network (PNN), Bayesian and Support Vector
Machine (SVM) classifiers. The best results achieved for the SVM classifier were 96, 98 and
97.6 for specificity, sensitivity and accuracy, respectively. 28.6% of the dataset was used as a
training set, while the remaining 72.4% was used for training. If more data had been used for
training, that could improve the performance.
Mahendran Gandhi et al. [6] used a gray-level co-occurrence matrix (GLCM) to extract the input
features that feed the SVM classifier. They tried to build an automated method by using
morphological operators and SVM classifiers on non-dilated color fundus retinal images to detect
the exudates. The used images were five fundus images in JPEG format with size 2196 x 1958
pixels. The SVM classifier was used to examine the disease’s severity, whether the effect on the
patient’s eye was moderate or severe. The used classifiers' results were that all five images were
diagnosed with an abnormality, with three severely affected by exudates and two moderately
affected.
Sohini Roychowdhury et al. [7] introduced a computer-aided screening system named DREAM.
DREAM uses fundus images collected from two databases: the DIARETDB1 dataset and the
MESSIDOR dataset in order to differentiate the DR images from the normal ones and generate a
severity grade. Some classifiers were used, such as AdaBoost, Support Vector Machine (SVM),
the Gaussian Mixture Model (GMM), and k-Nearest Neighbor (kNN). AdaBoost helped in the
reduction of extracted features to 30 selected features out of 78. The feature reduction decreased
the average computing time from 59.54s to 3.46s. The DREAM system achieved a sensitivity,
specificity and AUC of 100%, 53.1% and 0.904, respectively.
3. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
29
Ahmed El Tanboly et al. [8] developed a DR detection system by using OCT images in three
stages by using different segmentation and classification techniques. They extracted three main
features to quantify the following from the segmented OCT images: “reflectivity", “curvature",
and “thickness" of retinal layers. The segmented layers are characterized by a function used to
describe the random distribution, called cumulative-probability distribution function (CDF), of its
extracted features. The used classifier has been trained in order to select the distinctive features of
retinal layers and to detect the DR by using their CDFs. The system results were 83%, 92% and
100% for sensitivity, accuracy and specificity, respectively.
Mohammed Ghazal et al. [9] proposed a CADe system for detecting NPDR in the early stages by
using OCT images. The built system consists of four primary stages: preprocessing, feature
extraction, system training, then diagnosis and testing. The preprocessing stage contains the
segmentation of retinal OCT images into 12 layers. The extracted layers are aligned by using
layer number 6 outer nuclear layer ONL as reference. The output of the preprocessing stage is fed
into the convolutional neural networks (CNNs) for training and evaluation. The best results have
been acquired by using the proposed CNN, where the sensitivity, specificity and accuracy were
100%, 88% and 94%, respectively. It has not been revealed how accurate the alignment with the
y-axis is and how this affects the final result.
Peyman Gholami et al. [10] proposed an automated classification method to identify eyes with an
ocular disease like DR, Age-related Macular Degeneration (AMD) or Macular Hole (MH), or
normal eyes, from processing OCT images. The images were collected at Sankara Nethralaya
(SN) eye hospital, Chennai, India. The images were preprocessed by removing the noise by using
a wavelet-based denoising technique. Additionally, the images were down sampled from 512 x
1024 pixels into 500 x 750 pixels. The authors relied on extracting the Local Binary Pattern
(LBP) features to feed the used classifiers. The used feature selection reduced the used features
from 375000 to 16383 features. They used SVM, random forest and multiphase method
classifiers. The achieved result from classifying the normal and abnormal images was AUC
98.6%, where the used multiphase classifier achieved AMD, DR, and MH AUC as 100%, 95%
and 83%, respectively. The proposed system has perfect AMD detection, but it needs more
improvement for detecting MH.
Muhammad Awais et al. [11] worked on a system for separating Diabetic Macular Edema (DME)
OCT images from normal ones. The used images were collected from the Singapore Eye
Research Institute (SERI). They used a pre-trained CNN, and the features were extracted at
different layers by using a model involving visual graphic geometry with 16 weight layers
(VGG16). They carried out four experiments with noise removal, image cropping, both and
neither. The best results were obtained by using images with no noise removal or cropping and
applying the kNN classifier. The results were 93% accuracy, 87% sensitivity and 100%
specificity. The authors do not reveal the number of images used in the experiments.
Xuechen Li et al. [12] developed an automated system called “OCTD_Net” for separating DR
images from normal ones by using OCT images. The proposed system classifies whether the
image is normal or with DR and assigns value 1 for the DR patients with changes in the thickness
and reflection of retinal layers and 0 for DR images that do not display any significant changes.
The used features in their system were the optical reflection of retinal layers (gray-level intensity
of OCT images) and the retinal layers’ thickness (pixels). Softmax was the used classifier in their
experiment. The system was found to have a sensitivity of 0.90, an accuracy of 0.92 and a
specificity of 0.95. The advantage of this system compared with others is the ability to classify
the severity of DR where present.
4. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
30
Khaled Alsaih et al. [13] proposed a system for separating DME OCT images from normal ones.
They used 32 OCT volumes containing more than 3800 images. The used technique was based
on the evaluation of intraretinal cystoid space formation, hard exudates, retinal thickening, and
subretinal fluid. The used features were local binary pattern (LBP) features and extraction of a
histogram of oriented gradients within a multiresolution approach, bag of words (BoW)
representations and principal component analysis (PCA). The used classifiers were random forest
and SVM. The achieved sensitivity and specificity were both 87.50%. Because of the missing
detection of positive cases, the system is not reliable enough to be used in clinical applications.
Yo-Ping Huang et al. [14] proposed a method for detecting DR by using fundus images. They
ranked the DR attributes by applying a fuzzy analytical network process from most to least DR-
relevant factors. The transformed fuzzy neural network classifier was used to improve the
classification process. The associated rules among the selected attributes of DR were extracted to
reveal their degree of severity and importance. The used novel system with the newly proposed
models B and C has improved the classification quality for both training and testing sets where
the achieved AUC is 1.0.
Table 1. A comparison of the proposed methods by comparing the key feature(s) used and the
used classifiers.
Table 1. Comparison between the previous works
#
Author
s
Year Key Features Classifiers
1 [5] 2013
Thresholding
Morphological processing
algorithms
PNN
Bayesian
Classification
SVM
2 [6] 2013
entropy, contrast, correlation,
energy, homogeneity and
dissimilarity
SVM
3 [7] 2014 30 features used
GMM
kNN
SVM
AdaBoost
4 [8] 2017
Quantifying “reflectivity",
“curvature", and
“thickness"
Deep Fusion Classification
Network (DFCN)
5 [9] 2019 Patches extraction CNN
6 [10] 2018 LBP random forest with SVM
7 [11] 2017 - kNN
8 [12] 2019
Thickness and re-
flection of retinal layers
Softmax
9
[13]
2017 LBP
PCA
SVM
1
0
[14] 2019 -
Transformed Fuzzy
Neural Network
3. METHODOLOGY
In our project, we used 214 OCT retinal images: 107 images were normal retina images, and the
other 107 images were DR images. The images were selected to be 50% normal images and 50%
DR images. The whole DR images were selected and the normal images were selected randomly
from the dataset, whereas the first 107 images were selected. All used images were taken from
5. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
31
Scholars Portal Dataverse- University of Waterloo Dataverse- [15] - an open-source database
that contains different retinal OCT images-. The OCT images database was published by a group
of scholars from the University of Waterloo. The selected database contains OCT retinal images
for different diseases. The images are in a high resolution (jpeg format) that can be downloaded
and used with no need for preprocessing. The database contains more than 500 spectral-domain
OCT volumetric scans, divided into five datasets: Normal 206 images, Age-related Macular
Degeneration 55 images, Macular Hole 102 images, Diabetic Retinopathy 107 images and
Central Serous Retinopathy 102 images [16]. The images were collected from a raster scan
protocol with size of 512x1024 pixels. They were obtained from Sankara Nethralaya eye hospital,
India [17].
The normal images and DR images were downloaded from the database [18] and 214 images
were selected for the experiment. The dataset was divided into two groups: a group representing
about 75% of the images was used for classifiers’ training, while about 25% of images were used
for testing. Five statistical features were used to feed the classifiers with the required information
that helped distinguish between the DR and normal images. The used statistical features are the
matrix’s mean, standard deviation, mode, variance and median. Additionally, the mean of
derivatives, the standard deviation of derivatives, mode of derivatives and median of derivatives
were used. All these features were computed using MATLAB built-in functions. P-value for the
selected features is less than 5% (P-value <0.05).
The results were not satisfactory, and the system needed further improvement. Therefore, local
binary pattern (LBP) features were used, and the results displayed a significant improvement. The
LBP features detect the uniform local binary pattern of textures in gray-scale images [19].
Rotationally invariant feature information was used. 8 neighborhoods for each pixel were used in
the computation. There are many parameters for LBP features found in MATLAB, like the
number of neighbors, radius of circular pattern, rotation invariance flag, type of normalization
and interpolation method.
After adjusting the used features, the used classifiers SVM and kNN were also adjusted in order
to improve the classifiers’ performance by changing the SVM kernel function, polynomial-SVM
kernel order, SVM optimization routine, kNN neighbors and kNN distance metric. Fig. 1 presents
the flowchart for the used system. The used classifiers’ functions are built-in functions in
MATLAB.
In parallel to SVM and kNN classifiers, the Artificial Neural Network (ANN) has been used in
order to validate the result by using different approach. The used ANN has 21 input layers, 2
hidden layers and each one has 21 layers, and 1 out layer.
6. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
32
Figure 1. Flowchart of the used system
4. CLASSIFIERS
SVM is a widely-used supervised classifier developed by V. Vapnik and his team (AT&T Bell
Labs.). SVM can be used to train the linear, neural network, polynomial, or radial basis functions
classifiers [20]. SVM represents the data in space and gives binary classification to the classified
data. In this study, our system assigns the value 0 to normal images and 1 for DR images.
Different kernels can be used to enhance the classifier’s performance.
kNN is one of the common pattern recognitions methods used to classify data. It depends mainly
on nearby sample observation. The unclassified sample will be relying on the class of the class
represented by the majority of its k nearest neighbors in the training set [21].
ANN classifier is a model built to simulate the biological nervous system in humans. In the
biological neural network, the dendrites in neurons receive the signal and the axon transmit it to
other neurons [22]. In the ANN model, the input data is transmitted to hidden layers that contain
many neural networks. After the processing stage in hidden layers, the output is collected from
the output layer that has a prediction to the received information in the first stage. The hidden
layers are sets of weighted inputs and produce an output through an activation function. This
recurrent process extracts the features and generates the final output, which is the image
classification in this paper.
A summary of the used classifiers is found in Table 2.
7. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
33
Table 2. Summary of the used classifiers
5. RESULTS
The following performance parameters have been used in a confusion matrix in order to evaluate
the classifiers’ performance:
• True Positive (TP): event values with a correct prediction.
• False positive (FP): event values with incorrect predictions.
• True negative (TN): no-event values with a correct prediction.
• False negative (FN): no-event values with incorrect prediction.
The Accuracy (ACC) is the ratio of the total number of correct predictions, and it can be
calculated by the equation:
ACC = (TP+TN)/(TP+TN+FP+FN) (1)
The Sensitivity (Sens) is the ratio of positive cases that were correctly identified, as determined
using the equation:
Sens = TP/(TP+FN) (2)
Specificity (Spec) is the ratio of actual negative cases that were correctly predicted, as determined
using the equation:
Spec = TN/(TN+FP) (3)
Receiver Operator Characteristic (ROC) curve is a fundamental graphical tool used to show the
diagnostic performance of an operator (physician or machine) in two or more class problems. The
Area Under the ROC Curve (AUC) is commonly computed to give a sense of how good the
overall performance of the operator is over all the cases diagnosed. Table 3 presents the achieved
classifiers’ performance.
Table 3. The classifiers’ performance of training and testing datasets
Classifier Image group Sens% Spec% Acc% AUC%
Linear-SVM
Training 100 100 100 100
Test 100 100 100 100
RBF-SVM
Training 100 100 100 100
Test 100 100 100 100
Polynomial-SVM Training 100 100 100 100
Classifier Parameters
Support Vector Machine Linear, Radial Basis Function (RBF) and Polynomial
k-Nearest Neighbour
The number of nearest neighbours in the predictors are 1, 3
and 5.
NN Artificial Neural Network
8. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
34
(Kernel order 2) Test 100 96.43 98.15 98.15
Polynomial-SVM
(Kernel order 3)
Training 100 100 100 100
Test 100 96.43 98.15 98.15
kNN (k=1)
Training 100 100 100 100
Test 100 100 100 100
kNN (k=3)
Training 97.53 98.73 98.13 98.13
Test 100 100 100 100
kNN (k=5)
Training 100 100 100 100
Test 100 100 100 100
ANN
Training 100 100 100 100
Test 100 100 100 100
6. DISCUSSION
This paper discussed an automated system for detecting DR disease by using retinal OCT images.
Using the technology in the medical field can shorten the time, improve the quality and decrease
errors. Using the CADe system in diagnosing DR will assist in detecting the disease in the early
stages. It gives ophthalmologists the opportunity to treat or control the disease and save the
patients’ vision loss. The proposed system is capable of separating the OCT normal images from
the DR images with 100% accuracy.
The proposed system achieved 100% accuracy, which means all images classified as DR images
are valid abnormal cases. The system has 100% sensitivity, whereby it can detect all positive
cases without neglecting any positive cases or classifying the negative case as positive. In
addition, the system has the same performance in regard to detecting negative cases, where it
achieved 100% specificity. The proposed algorithm was executed in approximately 24 seconds
by a personal computer. The computer has 16GB RAM, Intel i7 with x64-bit processor with a
speed of 1.8GHz.
7. CONCLUSION
Diabetic retinopathy disease is one of the leading causes of vision loss. Early DR detection
prevents disease progression and helps ophthalmologists to treat or control the disease. In this
study, we have proposed an automated system that helps ophthalmologists to better diagnose DR
from OCT images. The proposed system is implemented in MATLAB R2018a. The system
displayed better performance than previous studies: the accuracy, sensitivity, specificity and
AUC were 100% by using SVM, kNN and ANN classifiers, as shown in Table 3. We found that
using features for detecting the images’ texture, such as the used features in our study (LBP
features) significantly improved the system performance because the retina OCT images have
common texture patterns.
We recommend further work to be applied on a vast number of images with more classification
levels by separating the normal OCT images from the abnormal ones and classifying the found
abnormality from ocular diseases. This work needs to use more sophisticated techniques, like
9. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
35
deep learning, in order to deal with a large amount of data. Our study is limited to the collected
images from the University of Waterloo. Therefore, we recommend applying the used approach
to different images group.
LIST OF ABBREVIATIONS
OCT: Optical Coherence Tomography; DR: Diabetic Retinopathy; LBP: local binary pattern;
ROC: receiver operating characteristic; AUC: Area Under Curve; CADe: computer-aided
detection; SVM: support vector machine; kNN k-Nearest Neighbor.
REFERENCES
[1] R. P. Singh, M. J. Elman, S. K. Singh, A. E. Fung, and I. Stoilov, "Advances in the treatment of
diabetic retinopathy," Journal of Diabetes and its Complications, p. 107417, 2019.
[2] A. C. S. Tan et al., "An overview of the clinical applications of optical coherence tomography
angiography," Eye, vol. 32, no. 2, pp. 262-286, 2018/02/01 2018.
[3] D. Huang et al., "Optical coherence tomography," science, vol. 254, no. 5035, pp. 1178-1181, 1991.
[4] T. E. de Carlo et al., "Detection of microvascular changes in eyes of patients with diabetes but not
clinical diabetic retinopathy using optical coherence tomography angiography," RETINA, vol. 35, no.
11, pp. 2364-2370, 2015.
[5] R. Priya and P. Aruna, "Diagnosis of diabetic retinopathy using machine learning techniques,"
ICTACT Journal on soft computing, vol. 3, no. 4, pp. 563-575, 2013.
[6] M. Gandhi and R. Dhanasekaran, "Diagnosis of diabetic retinopathy using morphological process and
SVM classifier," in 2013 International Conference on Communication and Signal Processing, 2013,
pp. 873-877: IEEE.
[7] S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, "DREAM: diabetic retinopathy analysis
using machine learning," IEEE journal of biomedical and health informatics, vol. 18, no. 5, pp. 1717-
1728, 2013.
[8] A. ElTanboly et al., "A computer‐aided diagnostic system for detecting diabetic retinopathy in optical
coherence tomography images," Medical physics, vol. 44, no. 3, pp. 914-923, 2017.
[9] M. Ghazal, S. Ali, A. Mahmoud, A. Shalaby, and A. S. El-Baz, "Accurate detection of non-
proliferative diabetic retinopathy in optical coherence tomography images using convolutional neural
networks," BioRxiv, p. 667865, 2019.
[10] P. Gholami, M. S. Hassani, M. K. Parthasarathy, J. S. Zelek, and V. Lakshminarayanan,
"Classification of optical coherence tomography images for diagnosing different ocular diseases," in
Multimodal Biomedical Imaging XIII, 2018, vol. 10487, p. 1048705: International Society for Optics
and Photonics.
[11] M. Awais, H. Müller, T. B. Tang, and F. Meriaudeau, "Classification of sd-oct images using a deep
learning approach," in 2017 IEEE International Conference on Signal and Image Processing
Applications (ICSIPA), 2017, pp. 489-492: IEEE.
[12] X. Li, L. Shen, M. Shen, F. Tan, and C. S. Qiu, "Deep learning based early stage diabetic retinopathy
detection using optical coherence tomography," Neurocomputing, vol. 369, pp. 134-144, 2019.
[13] K. Alsaih, G. Lemaitre, M. Rastgoo, J. Massich, D. Sidibé, and F. Meriaudeau, "Machine learning
techniques for diabetic macular edema (DME) classification on SD-OCT images," Biomedical
engineering online, vol. 16, no. 1, p. 68, 2017.
[14] Y.-P. Huang, H. Basanta, T.-H. Wang, H.-C. Kuo, and W.-C. Wu, "A Fuzzy Approach to
Determining Critical Factors of Diabetic Retinopathy and Enhancing Data Classification Accuracy,"
International Journal of Fuzzy Systems, vol. 21, no. 6, pp. 1844-1857, 2019.
[15] Optical Coherence Tomography Image Retinal Database. Available:
https://dataverse.scholarsportal.info/dataverse/OCTID
[16] P. Gholami, P. Roy, M. K. Parthasarathy, and V. Lakshminarayanan, "OCTID: Optical coherence
tomography image database," Computers & Electrical Engineering, vol. 81, p. 106532, 2020.
[17] P. Gholami, M. Kuppuswamy Parthasarathy, P. Roy, and V. Lakshminarayanan, "OCTID citation,"
V1 ed: Scholars Portal Dataverse, 2018.
[18] J. J. Salazar et al., "Anatomy of the Human Optic Nerve: Structure and Function," 2018.
10. Signal & Image Processing: An International Journal (SIPIJ) Vol.12, No.6, December 2021
36
[19] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture
classification with local binary patterns," IEEE Transactions on pattern analysis and machine
intelligence, vol. 24, no. 7, pp. 971-987, 2002.
[20] E. Osuna, R. Freund, and F. Girosit, "Training support vector machines: an application to face
detection," in Proceedings of IEEE computer society conference on computer vision and pattern
recognition, 1997, pp. 130-136: IEEE.
[21] T. Denoeux, "A k-nearest neighbor classification rule based on Dempster-Shafer theory," IEEE
transactions on systems, man, and cybernetics, vol. 25, no. 5, pp. 804-813, 1995.
[22] Y. Narayan, "Hb vsEMG signal classification with time domain and Frequency domain features using
LDA and ANN classifier," Materials Today: Proceedings, vol. 37, pp. 3226-3230, 2021.
AUTHORS
Eng. Marwan Aldahami M.Sc. Graduate Student of Biomedical Engineering at the
Dept. of Electrical and Computer Engineering, King Abdulaziz University, Jeddah,
Saudi Arabia. Bachelor’s degree in Biomedical Technology from King Saud
University, Riyadh, Saudi Arabia.
Dr. Umar S. Alqasemi, Associate Professor of Biomedical Engineering at the Dept.
of Electrical and Computer Engineering, King, Abdulaziz University, Jeddah 21589,
Saudi Arabia. PhD and MSc degree in Biomedical Engineering from UConn, Storrs,
USA. Research work in ultrasound, optical, and photoacoustic imaging, medical
imaging recognition, bioelectronics, and digital and analog signal and image
processing.