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Imaging methods by using computer techniques provide doctors assistance atany time and relieve their workload, especially for iterative processes likeidentifying objects of interest such as lesions and anatomical structures fromthe image. Detection of microaneurysms (MAs) as one of the lesions in theretina is considered to be a crucial step in some retinal image analysisalgorithms for the identification of diabetic retinopathy (DR) as thesecond-largest eye diseases in developed countries. The objective of this studyis to compare the effect of two preprocessing methods, IlluminationEqualization, and Top-hat transformation, on retinal images to detect MAs usinga combination of Matching based approach and deep learning methods either inthe normal fundus images or in the presence of DR. The steps for the detectionare as following: 1) applying preprocessing, 2) vessel segmentation andmasking, and 3) MAs detection using a combination of Matching based approachand deep learning. From the accuracy viewpoint, we compared the method tomanual detection performed by ophthalmologists for our big retinal imagedatabases (more than 2200 images). Using first preprocessing method,Illumination equalization and contrast enhancement, the accuracy of MAsdetection was about 90% for all databases (one local and two publicly retinaldatabases). The performance of the MAs detection methods using top-hatpreprocessing (the second preprocessing method) was more than 80% for alldatabases.

Automated Detection of Microaneurysms in Color Fundus Images using Deep Learning with Different Preprocessing Approaches