Anemopal: portable iron deficiency anemia pre-detection system via conjunctiva, nail beds, and palm pallor image processing using machine learning algorithms - Consulta, Mabel; Horique, Joy Rianne R.; Perez, Paul Carl C.; Profugo, Reggie J. 4 0 16; 5 (2024)
ABSTRACT: Anemia is a condition that refers to a lower-than-normal number of red blood cells or insufficient haemoglobin within the red blood cells. According to the World Health Organization, there were alarming rates of anemia in the Philippines in 2019. Almost half of the world's population experiences limited access to healthcare, often due to cost, transportation difficulties, or provider scarcity. This study presents the development of a portable iron deficiency anemia pre-detection system using machine learning algorithms.This study analyses the image of the conjunctiva, nailbebs, and palm pallor to detect different clinical grading of iron deficiency anemia. The detection models of the system include a trained Random Forest Algorithm, YOLOv8 instance segmentation, and RGB Thresholding image processing. The device incorporates a minicomputer with an integrated camera to capture the images of the pallor and a touchscreen monitor that displays the system results. The study gathered data from both anemic and non-anemic to assess the system's accuracy, precision, sensitivity, and specificity. The results indicate that the portable device successfully identified different clinical grades of Iron Deficiency Anemia with an accuracy rate of 75%. The respondents described the device as comfortable, convenient, and an accurate method for detecting anemia.
Undergraduate Thesis: (Bachelor of Science in Electronics Engineering) - Pamantasan ng Lungsod ng Maynila, 2024.
Keywords: 2 -- 0; 6; 20; 20; 2