Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Diabetic Retinopathy Detection Using Various Machine Learning Algorithms Description An account of the resource 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 &amp; Francis Group, LLC. Creator An entity primarily responsible for making the resource Banu P.K.N.; Sreekumar Y. Source A related resource from which the described resource is derived Machine Intelligence: Computer Vision and Natural Language Processing, pp. 113-128. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003424550-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003424550-6</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173312385&amp;doi=10.1201%2F9781003424550-6&amp;partnerID=40&amp;md5=df06b741144a6853a827e4ed673f9ca9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173312385&amp;doi=10.1201%2f9781003424550-6&amp;partnerID=40&amp;md5=df06b741144a6853a827e4ed673f9ca9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100096031-0; 978-103220199-3 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Banu P.K.N., Department of Computer Science, Christ University, Bangalore, India; Sreekumar Y., Department of Computer Science, Christ University, Bangalore, India