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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 %.<\/jats:p>","DOI":"10.3233\/jifs-189855","type":"journal-article","created":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T12:53:23Z","timestamp":1617713603000},"page":"5327-5335","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A Comparative analysis of stroke diagnosis from retinal images using hand-crafted features and CNN"],"prefix":"10.1177","volume":"41","author":[{"given":"R. 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