A Systematic Review of Detection of Diabetic Retinopathy using Machine Learning Approach

2021 ◽  
pp. 2783-2789
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Fundus Photography ◽  
Research Papers ◽  
Posterior Portion ◽  
Machine Learning Approach ◽  
Pros And Cons ◽  
Retinal Fundus

Diabetes Mellitus causes diabetic retinopathy (DR). It can cause blindness if not diagnosed early. Disease diagnosis is an essential and highly scrutinized biomedical field in which machine learning has been significantly used. Recently, machine learning has emerged as one of the most widely used approaches for improving performance in various areas, including medical image analysis and classification. This research compares several machine learning experiments based on the accuracy and sensitivity of retinal fundus pictures acquired by the fundus camera to assess several strategies for identifying Diabetic retinopathy. Inflammatory illnesses in the posterior portion of fundus photography are followed by retinal imaging. In particular, machine learning and deep learning are cutting-edge technologies well-suited for data analytics applications in the medical field. The results were compared to those of other approaches such as deep neural networks and other best practices. This work will be beneficial to researchers who want to apply their research in this field. During this research, we have gone through several research papers. This paper includes findings from other researcher’s studies, which have been summarized to present their pros and cons for disease diagnosis

scholarly journals Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant

2021 ◽  
Vol 12 (5) ◽  
pp. 810-817
Author(s):  
Sandhya N. dhage, Dr. Vijay Kumar Garg
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Cotton Plant ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Early Detection Of Disease ◽  
Cotton Crop ◽  
Machine Learning Approach ◽  
Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires  regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming  too late for diseases to be cured.  Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

scholarly journals Review on Machine Learning Techniques for Diagnosis of Heart Disease

2021 ◽  
Vol 9 (8) ◽  
pp. 1418-1420
Author(s):  
Harshal P. Sabale
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Human Health ◽  
World Health Organisation ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Heart Disease Diagnosis

Abstract: Now-a-days, heart disease is becoming a concern to human health. According to World Health organisation (WHO), heart disease is the number one killer among other fatal diseases. Excessive smoking, alcohol consumption and junk food are culprit for the heart disease. Physical inactivity is also a concerning to the human health. Heart disease is pretty hard to predict or diagnose using traditional methods like counselling. But, now-a-days, medical fields are using machine learning to predict or diagnose different diseases. Implementation of machine learning techniques provides faster and mostly accurate results. This can save many life. In this paper, different machine learning approach for heart disease diagnosis are reviewed. Keywords: Heart disease, CVD, Machine Learning

scholarly journals A Machine Learning Approach for the Diagnosis of Diabetes : A Review

2020 ◽  
pp. 509-516
Author(s):  
Pravin S. Rahate ◽  
Nikhat Raza
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Selection Process ◽  
High Sensitivity ◽  
Healthcare Services ◽  
Automated System ◽  
Retinal Images ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Patients With Diabetes

Diabetes mellitus (DM) is a chronic disease that affects 382 million patients’ worldwide (2013 data) and is predicted to increase to as many as 592 million adults by 2035. DM is one of the major causes of blindness in young adults around the world. The most serious ocular complication of DM is diabetic retinopathy (DR).Diabetic retinopathy is the most common microvascular complication in diabetes1, for the screening of which the retinal imaging is the most widely used method due to its high sensitivity in detecting retinopathy. Prompt diagnosis is important through efficient screening. The evaluation of the severity and degree of retinopathy associated with a person having diabetes is currently performed by medical experts based on the fundus or retinal images of the patient’s eyes As the number of patients with diabetes is rapidly increasing, the number of retinal images produced by the screening programmes will also increase, which in turn introduces a large labor-intensive burden on the medical experts as well as cost to the healthcare services. Manual grading of these images to determine the severity of DR is rather slow and resource demanding. This could be alleviated with an automated system either as support for medical experts’ work or as full diagnosis tool. This labor-intensive task could greatly benefit from automatic detection using machine learning technique. Early detection and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. The objective of this paper is to explore the work happening on the detection, progression and feature selection process for the prediction of DR and to establish the extent and depth of existing knowledge on RD prediction process.

scholarly journals Early Detection of Diabetic Retinopathy

2021 ◽  
Vol 9 (VI) ◽  
pp. 4181-4187
Author(s):  
P. L. N. Sowjanya
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Diabetic Retinopathy ◽  
Medical Imaging ◽  
Early Detection ◽  
Health Care System ◽  
Disease Diagnosis ◽  
Epidemiology Study ◽  
Speed Up ◽  
Care System

Diabetic retinopathy is one of the prevalent reasons of sight impairment in this day and age According to an epidemiology study, diabetic retinopathy affects one out of every three diabetics. In today's world, disease diagnosis is an essential part of medical imaging. In medical imaging, machine learning gives a greater vision for detecting disease. The objective is to detect diabetic retinopathy using ML. Machine learning in medical imaging could speed up and enhance the detection of sight caused by sugar. In order to detect diabetic retinopathy quickly and support the health-care system, this study will look at several machine learning methodologies, algorithms, and simulations. CNN is used to train the model.

scholarly journals A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

2021 ◽  
Vol 67 (1) ◽  
pp. 51-71 ◽  
Author(s):  
Mohamed Elhoseny ◽  
Mazin Abed Mohammed ◽  
Salama A. Mostafa ◽  
Karrar Hameed Abdulkareem ◽  
Mashael S. Maashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Multi Agent ◽  
Heart Disease Diagnosis

scholarly journals 3D Deep Learning on Medical Images: A Review

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5097 ◽  
Author(s):  
Satya P. Singh ◽  
Lipo Wang ◽  
Sukrit Gupta ◽  
Haveesh Goli ◽  
Parasuraman Padmanabhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Imaging Data ◽  
Learning Models ◽  
Processing Technologies ◽  
3D Cnn

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

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