International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
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Diabetic Retinopathy detection using Machine learning Anmol Ratan Tirkey1, Sony Tirkey2, Binod Adhikari3, Cazal Tirkey4 1Student,JAIN(Demmed-To-Be University), Bengaluru, Karnataka, India
2Student, CHRIST(Deemed-To-Be University), Bengaluru, Karnataka, India 3Researcher, Bengaluru, Karnataka, India 4Researcher, Bengaluru, Karnataka, India
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Abstract - According to the International Diabetes
It has been noted that there are variances in the ratio of ophthalmology-trained medical professionals to patients, which makes it difficult to diagnose and treat diabetic retinopathy. This is owing to the steady and gradual increase of diabetes around the world. As a result, problems with screening time, greater prices, decreased device sensitivity and specificity, etc., needed to be addressed. As a result, the introduction of automated detection systems based on standalone algorithms was suggested. The screening and diagnosis of DR are important study areas, and many researchers are working to advance these fields. Methodologies for image processing have made it simple and advantageous to examine acquired images, including their traits, behaviours, and processing.
Federation, there are currently over 470 million individuals diagnosed with diabetes globally, and by 2050, that number might rise to 700 million. There are two types of it: Type 1 and Type 2. Type 1 diabetes is chronic and incurable, however Type 2 diabetes can be cured if caught early. Identification of anomalies in retinal pictures is tough and complex in the medical sector since these signs of diabetes that affect the eyes appear to be very modest. Therefore, a non-invasive technique to uncover these abnormalities was required. After reviewing a number of research projects and developments, we attempt to highlight and explain the various methodologies used, their benefits and limitations, the general goal of the results, and the significance of a DR detection system. The survey also highlights the significance of early detection and the need to eliminate the factors that obstruct timely discovery.
Additionally, the development of new image processing techniques, classification algorithms, and neural networks has helped computer-aided detection systems perform quickly and has been recognised as a reliable option for applying analysis on retinal pictures. The aforementioned system models would seek to determine the necessity of referral for additional treatment and ongoing eye care. We won't be too far off from being able to apply these techniques on our cell phones given the speed at which technology is developing. To identify DR, extensive study has been done and numerous methods/processes have been developed. The implementation of automated screening systems and the outcomes obtained are characteristics of success and a potential failure. The effectiveness and complexity of the existing algorithms for the identification of DR employing the various image processing processes and their associated methodologies can yet be improved. Here, we want to provide you a general overview of how to create an application that can identify anomalies caused by DR in the retina of the eye.
Key Words: Diabetic Retinopathy (DR), Non-Invasive, Digital Image Processing
1.INTRODUCTION About 470 million people worldwide are diagnosed with diabetes. During the period spanning 2000-2016, a 5% increase in deaths related to diabetes was observed, with 1.6 million deaths each year. Diabetes can be considered a major public health problem in India. Its symptoms are indistinct, one can grow thirstier or become hungrier than usual, or we might not figure out why we’re more tired than usual. The meteoric rise in urbanization and industrialization, along with our lifestyle choices provides an ideal scenario for the prevalence of diabetes and its associated complications.
2. EXISTING SYSTEM The author of the research paper [1] employs feature extraction to locate and extract the details that are specific to the image. Speeded-Up Robust Features (SURF) detects blob features. It is a kind of detector algorithm that draws attention to particular locations on images that have been transformed into a coordinate system. The MSER (maximally stable extremal region) is used for matching. Extremal pixels are those that are either more or less
Fig -1: Normal Image Fundus diabetic retinopathy image
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