Recurrent Neural Network Architectures for Analysing Biomedical Data Sets

A Review of Automated Methods for the Detection of Sickle Cell Disease

Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes elaborate analysis of proper disease diagnosis after accurate detection followed by a classification of irregularities, which plays a vital role in the sickle cell disease diagnosis, treatment planning, and treatment outcome evaluation. Proper segmentation of complex cell clusters makes sickle cell detection more accurate and robust. Cell morphology has a key role in the detection of the sickle cell because the shapes of the normal blood cell and sickle cell differ significantly. This review emphasizes state-of-the-art methods and recent advances in detection, segmentation, and classification of sickle cell disease. We discuss key challenges encountered during the segmentation of overlapping blood cells. Moreover, standard validation measures that have been employed to yield performance analysis of various methods are also discussed. The methodologies and experiments in this review will be useful to further research and work in this area.