Machine Learning Driven Anemia Identification and Classification: A Comprehensive Survey

Abstract

Anemia, a common medical condition characterized by a deficiency in red blood cells or hemoglobin, presents a significant health concern globally. The development of accurate and efficient tools for the identification and classification of anemia is crucial for early diagnosis and targeted intervention. Machine learning, with its robust capabilities in pattern recognition, has emerged as a promising avenue for addressing this challenge. This literature survey paper provides a comprehensive overview of existing research in the field of anemia identification and classification using machine learning. The paper begins by delineating the epidemiological significance of anemia, underlining the pressing need for advanced diagnostic techniques. A systematic examination of the physiological basis of anemia, its types, and clinical symptoms sets the stage for understanding the complexity of anemia classification. We review traditional diagnostic methods and their limitations, emphasizing the potential for machine learning to augment and enhance these techniques. The primary objective of this survey is to synthesize existing knowledge in the field, identify common trends, and highlight advancements that have significantly contributed to the accuracy and efficiency of RBC classification methods. The survey is organized into several key sections, each exploring distinct aspects of previous research endeavors. The survey commences with an overview of the importance of RBC classification in medical diagnostics, emphasizing the critical role of automated techniques in improving efficiency and accuracy. It then delves into the fundamentals of image processing and CNNs, providing a foundation for understanding the methodologies discussed in subsequent sections. The core of the survey is dedicated to a comprehensive analysis of previous implementations, categorizing them based on their proposed models and datasets. Each section provides a detailed review of the methodology, highlighting the key contributions and innovations, and the datasets used for training and evaluation. Specific areas of focus include feature extraction techniques, network architectures, and classification algorithms employed in these projects. Furthermore, the survey addresses challenges encountered in RBC classification, such as dataset scarcity, class imbalance, and the need for robust and interpretable models. It also explores the potential for transfer learning and the integration of emerging technologies like deep learning and edge computing in this domain. In conclusion, this paper not only illuminates the advancements made in anemia detection but also underscores the gaps and opportunities for further research. It provides a valuable resource for researchers, medical practitioners, and data scientists seeking to enhance the accuracy and efficiency of RBC classification systems, ultimately contributing to the improvement of medical diagnostics and patient care.