AI-Based Early Prediction of Iron-Deficiency Anemia in Rural Women

Anemia is a common disease that may occur in individuals due to low levels of hemoglobin in the blood, whereas conventional diagnosis usually requires clinical examination and laboratory tests. Preliminary screening is often hindered by economic, availability, and even early recognition challenges. AnemiaVision is the proposed artificial intelligence system designed to recognize potential cases of anemia from conjunctiva images. AnemiaVision uses a Convolutional Neural Network to process and classify the image data into anemic and non-anemic classes. The model was trained using a dataset of conjunctiva images categorized into Anemic and Non-Anemic classes. The images were preprocessed and normalized before model training. AnemiaVision is comprised of the trained model and a web interface, which allows users to upload a conjunctiva image and receive screening results with model confidence. The system was implemented using Python programming language, TensorFlow/Keras framework, OpenCV computer vision library, and PHP web interface framework. The proposed system was experimentally evaluated, and results showed potential for rapid preliminary screening of anemia. AnemiaVision is proposed as an educational and screening tool, which cannot substitute laboratory tests and clinical diagnosis by medical professionals.