Automatic detection of pathological myopia using machine learning

• Published in: Scientific reports 2021-08, Vol.11 (1), p.16570-9, Article 16570
• Main Authors: Rauf, Namra, Gilani, Syed Omer, Waris, Asim
• Format: Article
• Language: English
• Subjects: 639/166/985; 692/700; Area Under Curve; Blindness; Datasets as Topic; Deep Learning; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; Learning algorithms; Liquefaction; Machine learning; Macular degeneration; Models, Neurological; multidisciplinary; Myopia; Myopia, Degenerative - diagnosis; Myopia, Degenerative - diagnostic imaging; Myopia, Degenerative - pathology; Neural networks; Predictive Value of Tests; ROC Curve; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-95205-1

Pathological myopia is a severe case of myopia, i.e., nearsightedness. Pathological myopia is also known as degenerative myopia because it ultimately leads to blindness. In pathological myopia, certain myopia-specific pathologies occur at the eye’s posterior i.e., Foster-Fuchs’s spot, Cystoid degeneration, Liquefaction, Macular degeneration, Vitreous opacities, Weiss’s reflex, Posterior staphyloma, etc. This research is aimed at developing a machine learning (ML) approach for the automatic detection of pathological myopia based on fundus images. A deep learning technique of convolutional neural network (CNN) is employed for this purpose. A CNN model is developed in Spyder. The fundus images are first preprocessed. The preprocessed images are then fed to the designed CNN model. The CNN model automatically extracts the features from the input images and classifies the images i.e., normal image or pathological myopia. The best performing CNN model achieved an AUC score of 0.9845. The best validation loss obtained is 0.1457. The results show that the model can be successfully employed to detect pathological myopia from the fundus images.