scholarly journals Harnessing Feature Extraction Techniques alongside CNN for Diabetic Retinopathy Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 422-425
Keyword(s):  
Diabetes Mellitus ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Medical Condition ◽  
Machine Learning Algorithms ◽  
Extraction Techniques ◽  
Energy Deficiency ◽  
Automated Screening ◽  
Processing Techniques

Diabetes mellitus is a disorder that inhibits your body from properly using the energy from the food you consume. The blood vessels and blood are responsible for the transport of sugar. A hormone called insulin helps cells to take in sugar to be used as energy. Deficiency in insulin causes the disease of diabetes mellitus. One of the side effect of diabetes mellitus is diabetic retinopathy. Diabetic retinopathy is the medical condition that causes the principal vision or in rare cases entire vision loss. Diabetic retinopathy has frequent occurrences in people among 20 to 60 years. Addressing this problem, we have developed an application that saves time and gives the result of the stage of the disease. This research paper presents a CNN based system that classifies the patients in four classes as 0-no DR, 1-Mild DR, 2-Moderate DR, 3-Severe DR. The system takes the input as an image taken from a fundus camera. Image processing techniques and machine learning algorithms are used for feature extraction. The Automated screening of the retinal images would assist the doctors to easily identify the patient's condition more precisely. With this we can easily distinguish between normal and abnormal images of the retina, this will reduce the number of inspections for the doctors.

scholarly journals Detection of Hard Exudates Based on Morphological Feature Extraction

2018 ◽  
Vol 11 (1) ◽  
pp. 215-225 ◽  
Author(s):  
Shilpa Joshi ◽  
P. T. Karule
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Morphological Feature ◽  
Vision Loss ◽  
Automated System ◽  
Diabetic Patients ◽  
Computationally Efficient ◽  
Image Processing Algorithm ◽  
Hard Exudates ◽  
Processing Techniques

In diabetic patients, the chances of vision loss are higher. These issues related to vision can be diagnosed using diabetic retinopathy. It is one of the very important diseases amongst all retinal pathologies. One of the simplest changes observed on the eye due to diabetes is lesions in yellow or white color i.e. hard exudates (EX). It appears bright in fundus images and hence it is the most important to detect using image processing algorithm. In this work the proposed algorithm used is based on morphological feature extraction. Post processing techniques are required to separate out EX from other bright artefacts such as cotton wool spot and optic disc. The performance evaluation of the proposed algorithm shows the sensitivity of 96.7%, specificity 85.4% and accuracy of 91% on image level detection on Diaretdb1 database and achieved higher accuracy on publicly available e-ophtha EX retinal image database in terms of lesion level detection. It is computationally efficient as an automated system to assist the ophthalmologist. Early detection of hard exudates is crucial for diagnosing the stages of diabetic retinopathy to prevent blindness.

Machine Learning Applications in Breast Cancer Diagnosis

2017 ◽  
pp. 465-490
Author(s):  
Syed Jamal Safdar Gardezi ◽  
Mohamed Meselhy Eltoukhy ◽  
Ibrahima Faye
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Extraction Techniques ◽  
Cad Systems ◽  
Cad System ◽  
Machine Learning Applications ◽  
Detection And Diagnosis

Breast cancer is one of the leading causes of death in women worldwide. Early detection is the key to reduce the mortality rates. Mammography screening has proven to be one of the effective tools for diagnosis of breast cancer. Computer aided diagnosis (CAD) system is a fast, reliable, and cost-effective tool in assisting the radiologists/physicians for diagnosis of breast cancer. CAD systems play an increasingly important role in the clinics by providing a second opinion. Clinical trials have shown that CAD systems have improved the accuracy of breast cancer detection. A typical CAD system involves three major steps i.e. segmentation of suspected lesions, feature extraction and classification of these regions into normal or abnormal class and further into benign or malignant stages. The diagnostics ability of any CAD system is dependent on accurate segmentation, feature extraction techniques and most importantly classification tools that have ability to discriminate the normal tissues from the abnormal tissues. In this chapter we discuss the application of machine learning algorithms e.g. ANN, binary tree, SVM, etc. together with segmentation and feature extraction techniques in a CAD system development. Various methods used in the detection and diagnosis of breast lesions in mammography are reviewed. A brief introduction of machine learning tools, used in diagnosis and their classification performance on various segmentation and feature extraction techniques is presented.

Construction of benchmark retinal image database for diabetic retinopathy analysis

2020 ◽  
Vol 234 (9) ◽  
pp. 1036-1048
Author(s):  
Jaskirat Kaur ◽  
Deepti Mittal
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Medical Condition ◽  
Diagnostic Technique ◽  
Performance Comparison ◽  
Fundus Images ◽  
Benchmark Database ◽  
Computer Aided ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy, a symptomless medical condition of diabetes, is one of the significant reasons of vision impairment all over the world. The prior detection and diagnosis can decrease the occurrence of acute vision loss and enhance efficiency of treatment. Fundus imaging, a non-invasive diagnostic technique, is the most frequently used mode for analyzing retinal abnormalities related to diabetic retinopathy. Computer-aided methods based on retinal fundus images support quick diagnosis, impart an additional perspective during decision-making, and behave as an efficient means to assess response of treatment on retinal abnormalities. However, in order to evaluate computer-aided systems, a benchmark database of clinical retinal fundus images is required. Therefore, a representative database comprising of 2942 clinical retinal fundus images is developed and presented in this work. This clinical database, having varying attributes such as position, dimensions, shapes, and color, is formed to evaluate the generalization capability of computer-aided systems for diabetic retinopathy diagnosis. A framework for the development of benchmark retinal fundus images database is also proposed. The developed database comprises of medical image annotations for each image from expert ophthalmologists corresponding to anatomical structures, retinal lesions and stage of diabetic retinopathy. In addition, the substantial performance comparison capability of the proposed database aids in analyzing candidature of different methods, and subsequently its usage in medical practice for real-time applications.

scholarly journals Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Silky Goel ◽  
Siddharth Gupta ◽  
Avnish Panwar ◽  
Sunil Kumar ◽  
Madhushi Verma ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Fundus Photography ◽  
Huge Amount ◽  
Early Screening ◽  
Image Metadata ◽  
Very High

Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy.

scholarly journals Survival Time of Visual Gains after Diabetic Vitrectomy and Its Relationship with Ischemic Heart Disease

2020 ◽  
Vol 17 (1) ◽  
pp. 310
Author(s):  
Abdah Khairiah Che Md Noor ◽  
Evelyn Li Min Tai ◽  
Yee Cheng Kueh ◽  
Ab Hamid Siti-Azrin ◽  
Zamri Noordin ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Visual Acuity ◽  
Diabetic Retinopathy ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Survival Time ◽  
Proliferative Diabetic Retinopathy ◽  
Vision Loss ◽  
Ischemic Heart ◽  
Vitrectomy Surgery

Vitrectomy surgery in proliferative diabetic retinopathy improves the vision-related quality of life. However, there is lack of data on the duration of maintenance of visual gains post vitrectomy. This study thus aimed to determine the survival time of visual gains and the prognostic factors of vision loss after vitrectomy surgery for complications of proliferative diabetic retinopathy. A retrospective cohort study was conducted in an ophthalmology clinic in Malaysia. We included 134 patients with type 2 diabetes mellitus on follow-up after vitrectomy for proliferative diabetic retinopathy. Visual acuity was measured using the log of minimum angle of resolution (LogMar). A gain of ≥0.3 LogMar sustained on two subsequent visits was considered evidence of visual improvement post vitrectomy. Subjects were considered to have vision loss when their post-operative visual acuity subsequently dropped by ≥0.3 LogMar. Kaplan–Meier analysis was used to determine the survival time of visual gains. Cox Proportional Hazard regression was used to determine the prognostic factors of vision loss. The median age of patients was 56.00 years (IQR ± 10.00). The median duration of diabetes mellitus was 14.00 years (IQR ± 10.00). Approximately 50% of patients with initial improvement post vitrectomy subsequently experienced vision loss. The survival time, i.e., the median time from surgery until the number of patients with vision loss formed half of the original cohort, was 14.63 months (95% CI: 9.95, 19.32). Ischemic heart disease was a significant prognostic factor of vision loss. Patients with underlying ischemic heart disease (adjusted HR: 1.97, 95% CI: 1.18, 3.33) had a higher risk of vision loss post vitrectomy, after adjusting for other factors. Approximately half the patients with initial visual gains post vitrectomy maintained their vision for at least one year. Ischemic heart disease was a poor prognostic factor for preservation of visual gains post vitrectomy.

scholarly journals Diabetic Retinopathy. Morphofunctional State of the Retina in Pancreas Recipients

2021 ◽  
Vol 18 (3) ◽  
pp. 459-469
Author(s):  
I. V. Vorobyeva ◽  
L. K. Moshetova ◽  
A. V. Pinchuk ◽  
E. V. Bulava ◽  
K. E. Lazareva ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Pancreas Transplantation ◽  
Patient Management ◽  
Molecular Mechanisms ◽  
Vision Loss ◽  
Retinal Damage ◽  
Common Complication ◽  
Immunological Markers

Diabetes mellitus (DM) is one of the most common and rapidly progressing diseases worldwide. Diabetic retinopathy (DR) is a common complication of diabetes and the main cause of vision loss in middle-aged and elderly people. The development and progression of DR is closely related to the duration of diabetes, hyperglycemia, and arterial hypertension. There is growing evidence that inflammation is one of the key links in the pathogenesis of diabetic retinal damage, but the exact molecular mechanisms remain to be known. Pancreas transplantation (PT) is currently the only effective treatment for diabetes that restores normal physiological glucose metabolism. Due to the limited number of PT surgeries associated with the severity of intra- and postoperative complications and the acute issue of organ donation, studies on the assessment of DR after PT are few and contradictory. There is a need for further studies of the DR state after PT with the study of the influence of risk factors, determination of the level of immunological markers and the use of modern instrumental research methods to create effective patient management regimens in the postoperative period.

scholarly journals Fuel Economy Prediction using Feature Engineering

2021 ◽  
pp. 149-152
Author(s):  
Yash Nadkarni ◽  
Siddhesh Deo ◽  
Aditya Patwardhan ◽  
Amey Ponkshe
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Fuel Economy ◽  
High Performance ◽  
Predictive Accuracy ◽  
Machine Learning Algorithms ◽  
Coolant Temperature ◽  
Feature Engineering ◽  
Extraction Techniques

The traditional way to calculate fuel economy is done by using odometer reading and fuel consumed by car to travel that particular distance. This is a very narrow approach as fuel economy is affected by a variety of factors in the real world. Features such as throttle response, engine temperature, coolant temperature, gross weight of vehicle, etc. have a huge influence on the fuel economy. In order to overcome this problem, we have tried to predict fuel economy based on various features extracted from telemetric data in our project. In order to achieve this, we have implemented various feature selection and feature extraction techniques by further analyzing them with the purpose of calculating the effectiveness of those features to achieve high performance of machine learning algorithms that ultimately improves the predictive accuracy of the classifier. This provides us with the information regarding the amount of influence a particular feature has on the overall fuel economy of the vehicle.

scholarly journals PREVALENCE, PATHOGENESIS, CLASSIFICATION, IMAGING, SCREENING AND MANAGE-MENT OF DIABETIC RETINOPATHY- A CRITICAL REVIEW

2021 ◽  
Vol 9 (7) ◽  
pp. 1433-1442
Author(s):  
Narender Chanchal ◽  
Kushagra Goyal ◽  
Divya Vij ◽  
Rajesh Kumar Mishra
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
International Diabetes Federation ◽  
Pathological Process ◽  
Heterogeneous Cluster ◽  
Keywords Diabetic Retinopathy ◽  
Screening And Surveillance ◽  
Working Age ◽  
Microvascular Alterations

Diabetic retinopathy is that the leading reason for sightlessness among people between twenty-five and seventyfour years older within the industrialised world. Diabetes mellitus (DM) includes a heterogeneous cluster of disorders of carbohydrate, protein, and metastasis manifesting hyperglycemia. Diabetic retinopathy could be microangiopathy ensuing from the chronic effects of the disease, and shares similarities with the microvascular alterations that occur in different tissues at risk of DM equivalent to the kidneys and also the peripheral nerves. Diabetic retinopathy is assessed into nonproliferative and proliferative stages. Nonproliferative diabetic retinopathy (NPDR) involves progressive intraretinal microvascular alterations that may result in, and a lot of advanced proliferative stages outlined by extraretinal neovascularization. Imaging modalities in common clinical use for the management of NPDR and DME embrace structure photography, fluorescein angiography (FA), and optical coherence tomography (OCT). The suggested schedule for screening and surveillance for NPDR reflects data concerning the epidemiology and natural history of the disease. Diabetic retinopathy could be a leading explanationfor vision loss in working-age Americans and a major cause of sightlessness worldwide. The International Diabetes Federation estimates that as several as 592 million individuals worldwide can have DM in 2035, a rise from or so 387 million people calculable to possess the disease in 2014. Here, we tend to present a review of the presentunderstanding and new insights into biochemical mechanisms within the pathological process in DR, classification, furthermore as clinical treatments for DR patients. Keywords: Diabetic retinopathy, diabetes mellitus, retinal degeneration, fluoresces in angiography, optical coher- ence tomography, VEGF, focal/grid laser photocoagulation.

scholarly journals Automatic Handwriting Feature Extraction, Analysis and Visualization in the Context of Digital Palaeography

2016 ◽  
Vol 30 (04) ◽  
pp. 1653001 ◽  
Author(s):  
Y. Liang ◽  
M. C. Fairhurst ◽  
R. M. Guest ◽  
M. Erbilek
Keyword(s):  
Feature Extraction ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Writer Identification ◽  
Extraction Techniques ◽  
Image Processing Techniques ◽  
Quantitative Measurements ◽  
Historical Manuscripts ◽  
Analysis System ◽  
Processing Techniques

Digital palaeography is an emerging research area which aims to introduce digital image processing techniques into palaeographic analysis for the purpose of providing objective quantitative measurements. This paper explores the use of a fully automated handwriting feature extraction, visualization, and analysis system for digital palaeography which bridges the gap between traditional and digital palaeography in terms of the deployment of feature extraction techniques and handwriting metrics. We propose the application of a set of features, more closely related to conventional palaeographic assesment metrics than those commonly adopted in automatic writer identification. These features are emprically tested on two datasets in order to assess their effectiveness for automatic writer identification and aid attribution of individual handwriting characteristics in historical manuscripts. Finally, we introduce tools to support visualization of the extracted features in a comparative way, showing how they can best be exploited in the implementation of a content-based image retrieval (CBIR) system for digital archiving.

scholarly journals Knowledge based Expert System for Predicting Diabetic Retinopathy using Machine Learning Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 19-27
Keyword(s):  
Diabetic Retinopathy ◽  
Medical Condition ◽  
Machine Learning Algorithms ◽  
Diabetes Diagnosis ◽  
Hard Exudates ◽  
Knowledge Based ◽  
Assisted Diagnosis ◽  
Total Blindness ◽  
Manual Testing ◽  
Selection Algorithms

Diabetic retinopathy (DR) is a medical condition that can affect the patient's retina and cause leaks in the blood due to diabetes mellitus. The increase in cases of diabetes limits existing manual testing capability. Today new algorithms are becoming very important for assisted diagnosis. Effective diabetes diagnosis can benefit the victims and reduce the negative harmful effects, including blindness. If not treated in a timely manner, this disorder can cause different symptoms from mild vision problems to total blindness. Early signs of DR are the hemorrhages, hard exudates, and micro-aneurysms (HEM) that occur in the retina. Timely diagnosis of HEM is important for avoiding blindness This paper presents PSO feature selection algorithms with three classifications for the detection of Diabetic retinopathy using python.

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