Affiliations: [a] Department of Electronics and Communication, College of Engineering Trivandrum, Kerala | [b] Department of Electronics and Communication, College of Engineering Trivandrum, Kerala | [c] College of Engineering Trivandrum, Kerala | [d] Department of Ophthalmology, Sree Gokulam Medical College and Research Foundation, Kerala
Correspondence:
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Corresponding author. R.S. Jeena, Research Scholar, Department of Electronics and Communication, College of
Engineering Trivandrum, Kerala. E-mail: jeenashilu@gmail.com.
Abstract: Stroke is a major reason for disability and mortality in most of the developing nations. Early detection of stroke is highly significant in bio-medical research. Research illustrates that signs of stroke are reflected in the eye and may be analyzed from fundus images. A custom dataset of fundus images has been compiled for formulating an automated stroke detection algorithm. In this paper, a comparative study of hand-crafted texture features and convolutional neural network (CNN) has been recommended for stroke diagnosis. The custom CNN model has also been compared with five pre-trained models from ImageNet. Experimental results reveal that the recommended custom CNN model gives the best performance by achieving an accuracy of 95.8 %.
