Diabetic Retinopathy Detection Using Various Machine Learning Algorithms
- Title
- Diabetic Retinopathy Detection Using Various Machine Learning Algorithms
- Creator
- Banu P.K.N.; Sreekumar Y.
- Description
- The advances in technologies have paved the way to generate huge amounts of data in a variety of forms. Machine learning techniques, accompanied by Artificial Intelligence with its challenging nature help in extracting meaningful information from such data. This will have a great impact on many sectors, such as social media analytics, construction and healthcare, etc. Computer-aided clinical decision-making plays a vital role in todays medical field. Hence, a high degree of accuracy with which machine learning algorithms can detect diabetic retinopathy is really in demand. Convolutional neural networks, a deep learning technique, have been used to recognize pathological lesions from images. Image processing and analytics methods are used and have been trained to recognize the significant complications of diabetes, which cause damage to the retina, diabetic retinopathy (DR). Though this condition does not show any symptoms in its early stages, it has to be screened, diagnosed and treated at the earliest or it may lead to blindness. Deep neural networks have proved successful in screening DR from retinal images and handling the risks that may arise due to the disease. This chapter focuses on detecting diabetic retinopathy in retinal images by using efficient image processing and deep learning techniques. It also attempts to investigate the requirements of image pre-processing techniques for diabetic retinopathy. Experiments are carried out by taking a set of retinal images and predicting the level of diabetic retinopathy on a scale of 0 to 4. Deep learning techniques like CNN and DenseNet are applied and tested. 2024 Taylor & Francis Group, LLC.
- Source
- Machine Intelligence: Computer Vision and Natural Language Processing, pp. 113-128.
- Date
- 2023-01-01
- Publisher
- CRC Press
- Coverage
- Banu P.K.N., Department of Computer Science, Christ University, Bangalore, India; Sreekumar Y., Department of Computer Science, Christ University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-100096031-0; 978-103220199-3
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Banu P.K.N.; Sreekumar Y., “Diabetic Retinopathy Detection Using Various Machine Learning Algorithms,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/18411.