Imaging and computer vision systems offer the ability to study quantitatively on human physiology. On contrary, manual interpretation requires tremendous amount of work, expertise and excessive processing time. This work presents an algorithm that integrates image processing and machine learning to diagnose diabetic retinopathy from retinal fundus images. This automated method classifies diabetic retinopathy (or absence thereof) based on a dataset collected from some publicly available database such as DRIDB0, DRIDB1, MESSIDOR, STARE and HRF. Our approach utilizes bag of words model with Speeded Up Robust Features and demonstrate classification over 180 fundus images containing lesions (hard exudates, soft exudates, microaneurysms, and haemorrhages) and non-lesions with an accuracy of 94.4%, precision of 94%, recall and f1-score of 94% and AUC of 95%. Thus, the proposed approach presents a path toward precise and automated diabetic retinopathy diagnosis on a massive scale.
Abramoff, M.D., Niemeijer, M., Suttorp-Schulten, M.S.A., Viergever, M.A., Russell, S.R. and van Ginneken, B. (2008) Evaluation of a System for Automatic Detection of Diabetic Retinopathy from Color Fundus Photographs in a Large Population of Patients with Diabetes. Diabetes Care, 31, 193-198. https://doi.org/10.2337/dc07-1312
Usher, D., Dumskyj, M., Himaga, M., Williamson, T.H., Nussey, S. and Boyce, J. (2004) Automated Detection of Diabetic Retinopathy in Digital Retinal Images: A Tool for Diabetic Retinopathy Screening. Diabetic Med., 21(1), 84-90.
Frame, A.J., et al. (1998) A Comparison of Computer Based Classification Methods Applied to the Detection of Microaneurysms in Ophthalmic Fluorescein Angiograms. Comput. Biol. Med., 28, 225-238.
Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M.S.A., and Abràmoff, M.D. (2005) Automatic Detection of Red Lesions in Digital Color Fundus Photographs. IEEE Trans. Med. Imag., 24, 584-592.
Perumalsamy, N., Prasad, N.M., Sathya, S. and Ramasamy, K. (2007) Software for Reading and Grading Diabetic Retinopathy: Aravind Diabetic Retinopathy Screening 3.0. Diabetes Care, 30, 2302-2306. https://doi.org/10.2337/dc07-0225
Usher, D., Dumskyj, M., Himaga, M., Williamson, T.H., Nussey, S. and Boyce, J. (2004) Automated Detection of Diabetic Retinopathy in Digital Retinal Images: A Tool for Diabetic Retinopathy Screening, Diabetic Med., 21, 84-90.
Morales, S., Engan, K., Naranjo, V. and Colomer, A. (2017) Retinal Disease Screening through Local Binary Patterns. IEEE Journal of Biomedical and Health Informatics, 21, 184-192. https://doi.org/10.1109/JBHI.2015.2490798
Seoud, L., Hurtut, T., Chelbi, J., Cheriet, F. and Langlois, J.M.P. (2016) Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening. IEEE Trans-actions on Medical Imaging, 35, 1116-1126.