A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images. (May 2019)

• Record Type: Journal Article
• Authors: García-Floriano, Andrés; Ferreira-Santiago, Ángel; Camacho-Nieto, Oscar; Yáñez-Márquez, Cornelio
• Is Part Of: Computers & electrical engineering. Volume 75(2019)
• Page Start: 218
• Page End: 229
• Publication Date: 2019-05
• Subjects: Age-related macular degeneration -- Mathematical morphology -- Hu invariant moments -- Feature selection -- Support vector machines; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.11.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Age-Related Macular Degeneration (AMD) is a dangerous, chronic, and progressive illness that mostly affects people over 60 years old. This disease is related to the appearance of drusen: deposits of extracellular material located in the macular region. One way to effectively and non-invasively pre-diagnose AMD is by detecting the presence of drusen in fundus images. In this work we propose a new method that combines Digital Image Processing, Mathematical Morphology and a robust and powerful Machine Learning model: a Support Vector Machine (SVM). The enclosed macular region is subjected to a contrast enhancement method, followed by the application of basic morphological operations. We use invariant moments as the features of the processed image. The resulting vector is classified by an SVM as positive or negative for drusen. The proposed method is able to discriminate between healthy and afflicted cases with a classification accuracy that outperforms many well-regarded state-of-the-art methods.