Early detection of diabetic retinopathy from big data in hadoop framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

Download Full-text

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519415500852 ◽

2015 ◽

Vol 15 (05) ◽

pp. 1550085 ◽

Cited By ~ 2

Author(s):

MADHURI TASGAONKAR ◽

MADHURI KHAMBETE

Keyword(s):

Diabetic Retinopathy ◽

Early Detection ◽

Ground Truth ◽

Vital Role ◽

Detection Accuracy ◽

Fuzzy C Means ◽

Fundus Imaging ◽

Imaging Detection ◽

Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

Download Full-text

Analysing Big Data in VANET via HADOOP Framework

Journal of Computer Science & Systems Biology ◽

10.4172/jcsb.1000281 ◽

2018 ◽

Vol 11 (04) ◽

Author(s):

Rahul Kumar Chawda ◽

Ghanshyam Thakur

Keyword(s):

Big Data ◽

Hadoop Framework

Download Full-text

Automatic exudate extraction for early detection of Diabetic Retinopathy

2013 International Conference on Information Technology and Electrical Engineering (ICITEE) ◽

10.1109/iciteed.2013.6676206 ◽

2013 ◽

Cited By ~ 8

Author(s):

Syna Sreng ◽

Jun-ichi Takada ◽

Noppadol Maneerat ◽

Don Isarakorn ◽

Bundit Pasaya ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Early Detection

Download Full-text

Diabetic Vitrectomy

The Eye and Foot in Diabetes ◽

10.5772/intechopen.91360 ◽

2020 ◽

Author(s):

Ogugua N. Okonkwo

Keyword(s):

Diabetic Retinopathy ◽

Early Detection ◽

Visual Impairment ◽

Surgical Techniques ◽

Surgical Removal ◽

Disease Monitoring ◽

Advanced Stage ◽

Improved Outcomes ◽

Diabetic Vitrectomy ◽

Proliferative Disease

Diabetic retinopathy (DR) in its advanced stage is a leading cause of blindness and visual impairment. Despite efforts at early detection of DR, disease monitoring, and medical therapy, significant proportions of people living with diabetes still progress to develop the advanced proliferative disease, which is characterized by neovascularization, actively proliferating fibrovascular membranes, and retinal traction. The surgical removal of this proliferating tissue and the treatment of the retinal ischemic drive can be very rewarding, providing significant stability of the retina and in several cases improved retinal anatomy and vision. Diabetic vitrectomy comprises a broad range of surgical techniques and maneuvers, which offer the surgeon and patient opportunity to reverse deranged vitreoretinal anatomy and improve or stabilizes vision. Advances in vitreoretinal technology have contributed greatly to more recent improved outcomes; it is expected that future advances will offer even more benefit.

Download Full-text

Predictive Models on Early Detection of Mental Health Problems using Big Data and Artifical Intelligence

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2019.5555 ◽

2019 ◽

Vol 7 (5) ◽

pp. 3377-3383

Author(s):

Dhruvesh Shah

Keyword(s):

Mental Health ◽

Big Data ◽

Early Detection ◽

Predictive Models ◽

Mental Health Problems ◽

Health Problems ◽

Artifical Intelligence

Download Full-text

Analysis of Health Insurance Big Data for Early Detection of Disabilities: Algorithm Development and Validation (Preprint)

10.2196/preprints.19679 ◽

2020 ◽

Author(s):

Seung-Hyun Jeong ◽

Tae Rim Lee ◽

Jung Bae Kang ◽

Mun-Taek Choi

Keyword(s):

Big Data ◽

Health Insurance ◽

Early Detection ◽

Developmental Delays ◽

Brain Lesion ◽

Detection Model ◽

Clinical Diagnoses ◽

Significant Performance ◽

Clinically Significant ◽

Age Range

BACKGROUND Early detection of childhood developmental delays is very important for the treatment of disabilities. OBJECTIVE To investigate the possibility of detecting childhood developmental delays leading to disabilities before clinical registration by analyzing big data from a health insurance database. METHODS In this study, the data from children, individuals aged up to 13 years (n=2412), from the Sample Cohort 2.0 DB of the Korea National Health Insurance Service were organized by age range. Using 6 categories (having no disability, having a physical disability, having a brain lesion, having a visual impairment, having a hearing impairment, and having other conditions), features were selected in the order of importance with a tree-based model. We used multiple classification algorithms to find the best model for each age range. The earliest age range with clinically significant performance showed the age at which conditions can be detected early. RESULTS The disability detection model showed that it was possible to detect disabilities with significant accuracy even at the age of 4 years, about a year earlier than the mean diagnostic age of 4.99 years. CONCLUSIONS Using big data analysis, we discovered the possibility of detecting disabilities earlier than clinical diagnoses, which would allow us to take appropriate action to prevent disabilities.

Download Full-text