Automatic Anemia Detection for Early Identification and Categorization using Machine Learning

Abstract

Anemia, characterized by a decrease in red blood cells or hemoglobin levels, is a prevalent global health issue affecting millions of individuals. Prompt and accurate diagnosis of Anemia is crucial for effective treatment and prevention of allied complications. This research work involves developing a digital image processing technique to analyse digital images of blood smears provided by patients. The algorithm will identify and measure red blood cell morphological abnormalities such as variations in size, shape, and color indicates different types of Anemia. To use machine learning methods for feature extraction and classification, the system will be trained using a vast collection of annotated blood smear images. Additionally, clinical data including patient demographics, medical histories, and laboratory test results will be integrated to enhance the algorithm's diagnostic accuracy and predictive capabilities. The proposed method aims to provide healthcare professionals with a reliable, cost-effective, and non-invasive tool for early detection and classification of Anemia. This initiative has the potential to significantly enhance patient outcomes and alleviate the burden of Anemia related complications on healthcare systems worldwide through the utilization of advancements in digital imaging and machine learning technologies.