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AmritaSync: A Deep Learning based Anaemia Tracker for Non-Invasive Detection

Publication Type : Conference Proceedings

Publisher : IEEE

Source : International Conference on Inventive Computation Technologies (ICICT)

Url : https://ieeexplore.ieee.org/abstract/document/10544545

Campus : Coimbatore

School : School of Physical Sciences

Department : Food Science and Nutrition

Year : 2024

Abstract : The World Health Organization identifies Anaemia as a health hazard condition affecting a quarter of the total world population, necessitating automated, quick, and reliable detection. To address this, an application was designed to capture eye and fingernail images using a mobile phone and classify them as anaemic or non-anaemic with accuracy as high as 91. A prepossessing pipeline was also presented for accurate localization and extraction of the eye and fingernail region from the input image. The proposed system has the potential to provide fast and efficient Anaemia diagnosis, with a recommendation system for patients to select a diet or consult a doctor. Additionally, the application shows the exact region filtered by the CNN algorithm.

Cite this Research Publication : Chalicham, Divyanth, Amrutham Varshit Purna, Alla Venkata Siddhartha, G. Jeyakumar, PR Janci Rani, Selvanayaki Kolandapalayam Shanmugam, and Senthil Kumar Thangavel, AmritaSync: A Deep Learning based Anaemia Tracker for Non-Invasive Detection, International Conference on Inventive Computation Technologies (ICICT),2024.

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