Development of portable and robust cataract detection and grading system by analyzing multiple texture features for Tele-Ophthalmology

Abstract

This paper presents a low cost, robust, portable and automated cataract detection system which can detect the presence of cataract from the colored digital eye images and grade their severity. Ophthalmologists detect cataract through visual screening using ophthalmoscope and slit lamps. Conventionally a patient has to visit an ophthalmologist for eye screening and treatment follows the course. Developing countries lack the proper health infrastructure and face huge scarcity of trained medical professionals as well as technicians. The condition is not very satisfactory with the rural and remote areas of developed nations. To bridge this barrier between the patient and the availability of resources, current work focuses on the development of portable low-cost, robust cataract screening and grading system. Similar works use fundus and retinal images which use costly imaging modules and image based detection algorithms which use much complex neural network models. Current work derives its benefit from the advancements in digital image processing techniques. A set of preprocessing has been done on the colored eye image and later texture information in form of mean intensity, uniformity, standard deviation and randomness has been calculated and mapped with the diagnostic opinion of doctor for cataract screening of over 200 patients. For different grades of cataract severity edge

The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

“…With the advancement and widespread adoption of Internet of Things (IoT) technology, the interconnection of various devices and sensors has brought significant opportunities to the field of education [1][2][3]. English, as a globally used second language, faces limitations in its teaching and assessment methods, including subjectivity, inefficiency, and lack of personalization [4][5][6]. Therefore, from the perspective of IoT, the research on the application of Recurrent Neural Network (RNN) in English grading, combined with deep learning, is of great significance.…”

An Assessment of Contemporary Methods and Data-Enabled Approaches for Early Cataract Detection

“…51,[60][61][62] It is critical to recognise that these imaging modalities should be used in conjunction with other diagnostic tools and that a full eye examination performed by a skilled ophthalmologist is still the gold standard in cataract diagnosis. [63][64][65][66][67] Furthermore, the incorporation of AI and machine learning algorithms shows promise in terms of increasing cataract detection; however, further study and validation are needed to ensure clinical application and efficacy. 34,[68][69][70] In terms of patient characteristics, there were no age or sex restrictions on the inclusion criteria.…”