Deep learning enabled hemorrhage detection in retina with DPFE and splat segmentation in fundus images

•Developed a Deep Learning Enabled Retinal Hemorrhage Detection Technique for the detection of retinal hemorrhage in retinal Fundus Images and it enhanced the classification accuracy of retinal fundus images for the detection of Diabetic Retinopathy (DR).•The proposed model combines Double Pierced Feature Extraction (DPFE), Enhanced Long Short-Term Memory (ELSTM) with Convolutional Neural Network (CNN), and the Maximally Stable Extremal Regions (MSER) algorithm for feature selection and classification.•DPFE approach shows the ability to detect different regions at multiple scales in a single pass, regardless of their shape profile. This characteristic significantly improves the accuracy of classification. Additionally, the analysis of feature subsets highlights the importance of specific features in predicting DR, providing valuable insights into the underlying characteristics of the disease.•Deep Learning Enabled Retinal Hemorrhage Detection Technique obtained an optimum output compared with ANFIS Classification with Cuckoo, ANFIS Classification with PSO, and Motion Pattern Recognition in the existing methodologies and attained maximum accuracy, specificity and sensitivity for the early diagnosis of retinal hemorrhages. The range of diabetics, hypertension, occlusions in vascular are rapidly increasing in the modern era. Adversarial effects of these diseases are the organ damage which is increasing from child to old age people. One of the severe damages involves the causes of eye diseases like diabetic eye disease or Diabetic Retinopathy (DR). Late prediction of DR may lead to permanent loss of vision. Hence there is a need among ophthalmologists in detecting and treating the eye diseases at the earliest stage. Detection of abnormalities as early as possible is a crucial task in today world as the existing strategies possess some setbacks. In this research work, a deep learning framework has been developed for the betterment in the prediction of retinal hemorrhage with the fundus image. The Double Pierced Feature Extraction (DPFE) is planned by merging the Enhanced Long Short-Term Memory (ELSTM) CNN and Maximally Stable Extremal Regions (MSER) algorithm. Splat algorithm is focused for segmentation which partitions a retinal image into various segments called splats of similar color, similar intensity and the spatial position. Every splat carries several details from that diverse features can be extracted. This splat based segment establishes a boundary ba