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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. 6

By: Consulta, Mabel; Horique, Joy Rianne R.; Perez, Paul Carl C.; Profugo, Reggie J. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; June 2024.46Edition: Description: 28 cm. 186 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: 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. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	TK2000 .M33 2024 (Browse shelf)	Available		FT7902

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Undergraduate Thesis: (Bachelor of Science in Electronics Engineering) - Pamantasan ng Lungsod ng Maynila, 2024. 56

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.

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