A hybrid fuzzy logic and convolution neural network (FIS-CNN) for automatic detection and classification of objects in comet assay images

Single cell gel electrophoresis, also known as comet assay, has been widely used for assessing the effect of genotoxicity and detecting deoxyribonucleic acid damage of individual eukaryotic cells. Imaging processes technique is convenient for discovering deoxyribonucleic acid damaged in the early stages. because it is one of the very important topics of our time, which is responsible for diagnosing many diseases at an early date, as well as knowing the stages of disease development by determining the degree of damage to the deoxyribonucleic acid.Deep learning algorithms were able to discover many complex features in large data sets, as manually extracting features may lower the accuracy of the information in addition to wasting time, especially in huge databases, so researchers have tended to use convolutional networks to detect and classify objects in images instead of methods Former traditional. Detection of deoxyribonucleic acid damage is one of the very important topics of our time because it is responsible for diagnosing many diseases at an early date, as well as knowing the stages of disease development by determining the degree of damage to the deoxyribonucleic acid. This study is suggest a hybrid Mamdani fuzzy logic (Type-2) with convolution neural network for detecting edges of each object of the image in model based on preprocessing image enhancement using adaptive histogram equalization and segmenting processing in morphology operations for each object in images, then patterns of comets are detected in convolution neural network and classify into five scores grade automatically. The experimental results conducted on the database have achieved a high performance precision 94.34 'percentage' accuracy, the propose approach compared to similar modern methods. In addition, the proposed approach is capable of detecting comets that are difficult to see with the human eye.