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Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system’s anatomical abnormalities. Standardized cerebellar assessments from large-scale clinical datasets are required to investigate correlations between the growing cerebellum and postnatal neurodevelopmental results. These studies could uncover structural abnormalities that could be employed as indicators to forecast neurodevelopmental and growth consequences. To achieve this, higher-throughput, precise, and impartial measures must be used to replace the existing human, semiautomatic, and advanced algorithms, which seem to be time-consuming and inaccurate. In this article, we presented an innovative deep learning (DL) technique for automatic fetal cerebellum segmentation from 2-dimensional (2D) US brain images. We present ReU-Net, a semantic segmentation network tailored to the anatomy of the fetal cerebellum. Moreover, we use U-Net as a foundation models with the incorporation of residual blocks and Wiener filter over the last 2 layers to segregate the cerebellum (c) from the noisy US data. 590 images for training and 150 images for testing were taken; also, we employed a 5-fold cross-assessment method. Our ReU-Net scored 91%, 92%, 25.42, 98%, 92%, and 94% for Dice Score Coefficient (DSC), F1-score, Hausdorff Distance (HD), accuracy, recall, and precision, correspondingly. The suggested method outperforms the other U-Net predicated techniques by a quantitatively significant margin ( p   0.001 ). Our presented approach can be used to allow high bandwidth imaging techniques in medical study fetal US images as well as biometric evaluation on a broader scale in fetal US images.

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Diabetes is a worldwide disease which is one of the main reason for blindness in the older age of any human community world-wide. Advanced level of diabetes leads to retinal hemorrhage. There is no efficient algorithm to detect the presence of hemorrhage. We have surveyed many algorithms and also recognized their efficiency. A new algorithm is proposed to detect the presence of hemorrhage with maximum efficiency and accuracy. The algorithm works by partitioning the image into differentiated segments covering the entire retinal image. These segments are denoted by splats. Each splat here establishes a set of information which helps us to extract the appropriate boundary. The pixels are grouped by the similarity of the color, intensity and spatial location. Retinal ischemia and weak blood vessels are the main reasons for the occurrence of haemorrhage. The new algorithm is based on the segmentation which is grouped by the colour, intensity and the spatial of the entire image.