Abstract
The services of health care play an important role in smart cities, which is the future reality of the current age. The automatic recognition of the Optic Disc (OD) region is an essential step in automated retinal image analysis. Generally, OD needs to be excluded for detection of exudates, the major symbols of diabetic retinopathy. The mobile-cloud assisted classification of OD from color fundus images can improve classification accuracy and OD’s recolonization speed. In this research, we propose a smart-phone based cloud-assisted resource aware framework aimed to detect and classify OD from fundus images by using a well trained classification procedure in cloud. The proposed technique comprised of shape based features trained by Naïve Bayes classifier in the cloud for OD and non-OD class. Experiments are performed on three online available datasets and a real dataset developed at local hospital with different contrast, illumination and abnormality. The proposed technique achieves an accuracy of 98.25% in classification of OD.
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Acknowledgements
The authors would like to thank teams of DIARETDB0, DIARETDB1, DRIVE and Al-Shifa Trust Eye Hospital Kohat, Khyber Pakhtunkhwa datasets for providing these datasets available to researchers. Furthermore, we would like to thank Dr. Irfan Aslam, Ophthalmologist / Medical Superintendent at Al-Shifa Trust Eye Hospital Kohat, Khyber Pakhtunkhwa, Pakistan for taking the time to establish the ground truth images. This work was supported by National Natural Science Foundation of China (NSFC) under Grant 61771378.
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Ullaha, H., Islam, N., Jan, Z. et al. Optic disc segmentation and classification in color fundus images: a resource-aware healthcare service in smart cities. J Ambient Intell Human Comput (2018). https://doi.org/10.1007/s12652-018-0988-8
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DOI: https://doi.org/10.1007/s12652-018-0988-8