Microaneurysms detection in fundus images using local Fourier transform and neighbourhood analysis

Microaneurysms, tiny, circular red dots that occur in retinal fundus images, are one of the earliest symptoms of diabetic retinopathy. Because microaneurysms are small and delicate, detecting them can be difficult. Their small size and cunning character make automatic detection of them difficult. The automatic detection of microaneurysms in retinal fundus images is proposed in this research using a local Fourier transform and neighbourhood analysis-based multi-scale approach technique. The suggested method is broken down into three stages: image preprocessing, the detection of retinal vessels and microaneurysm candidates, and labelling of the candidates. A multi-scale framework is used to develop every stage of the algorithm, with the exception of the initial image preprocessing, giving the mechanism for efficient microaneurysms detection. In contrast to the short-time Fourier transform, which extracts the neighbourhood of each pixel and calculates each local Fourier transform separately, the local Fourier transform is employed in this study to extract the MA. After that, neighbourhood analysis is performed to name the microaneurysm because the item is actually a collection of independent little images rather than the entire image. Three separate data sets and different types of performance indicators are used to examine the robustness of the proposed model. Through the prominent performance, the proposed model is able to outperform other existing models. The classification accuracy of the proposed method for MESSIDOR and ORIGA data set is 99.28% and 98.95%, respectively.