Advancements in Machine Learning and Deep Learning Techniques for Sickle Cell Disease Detection using Digital Morphology

Sickle Cell Disease (SCD) is still a serious public health concern, particularly in areas with inadequate access to healthcare. The management of the condition depends on an early and precise diagnosis; yet, traditional diagnostic techniques, such manual blood smear examinations, are frequently laborious, subjective, and prone to variability. The major developments in deep learning (DL) and machine learning (ML) approaches that are redefining SCD detection especially with regard to the application of digital morphology are examined in this review paper. The sickle-shaped red blood cells from microscopic pictures can effectively categorized by utilizing convolutional neural networks (CNNs), transfer learning, and ensemble learning techniques. This method offers better accuracy, speed, and scalability over conventional approaches. These models allow for more accurate assessments of cell morphology in addition to improving the accuracy of SCD diagnosis, lowering the possibility of human error. Additionally, issues like interpretability and data imbalance, offering answers via creative model designs and pre-processing methods. This research demonstrates the revolutionary potential of machine learning and deep learning in the healthcare industry, where intelligent, automated technologies may supplement clinical knowledge and improve outcomes for SCD patients worldwide. By combining these technologies, scalable and easily available diagnostic tools could be developed in the future, leading to more fair healthcare delivery.