Work

A Comprehensive Study of Supervised Learning Models on the OCTID Dataset for Retinal Disease Detection with Future Directions

Year: 2024

Type: article

Abstract: This paper presents a critical analysis of recent approaches to supervised learning techniques on the OCTID database that are important in the early diagnosis of retinal diseases such as AMD, CSR, DME, and MH. The review concentrates on methodologies, experimental configurations, and assessments of various machine learning and deep learning techniques. The limitations observed in the current work are in terms of computational complexity, possible limitation in labeling, and lack of extensive experimentation using videos with OCT. For future work, we suggest using semi-supervised learning as they are more accurate and less costly in medical applications.

Source: International Journal of Multicultural and Multireligious Understanding

DOI: 10.18415/ijmmu.v11i11.6403

Authors: Arman Mohammad Nakib, A K M Jayed, Shah Anisur Rahim Scene, Md Badsha

Institutions: Nanjing University of Information Science and Technology, Nanjing University of Science and Technology, Nanjing University of Posts and Telecommunications

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4404686362

FWCI:

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