{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fingertip-video-dataset-for-non-invasive","title":"Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier","arxiv_id":null,"date":"2024-05-08","proceeding":"IEEE Access 2024 5","authors":["HUMERA SABIR","KIFAYAT ULLAH KHAN","OMER ISHAQ","ABDULWAHAB ALAZEB","HANAN ALJUAID","ASAAD ALGARNI","JEONGMIN PARK"],"abstract":"Hemoglobin is the iron containing protein in red blood cells which carries oxygen from lungs to\r\nrest of the body tissues. Accurate measurement of hemoglobin is essential for diagnosing anemia, a condition\r\ncharacterized by a deficiency of red blood cells. This measurement is particularly vital before initiating\r\nblood transfusions for thalassemia patients. Non-invasive estimation of hemoglobin levels can be achieved\r\nthrough photoplethysmography (PPG)-based methods. PPG is an optical method to measure blood volume\r\nchanges in successive heart beats. PPG signals can be obtained from fingertip videos using a light source\r\nand a photodetector. SmartphonePPG utilizes a smartphone’s flashlight as a light source and its camera as\r\na photodetector to acquire PPG signals, offering an affordable and portable point-of-care tool. Despite the\r\nubiquity of smartphones, signals from their cameras often contain noise, making feature selection from PPG\r\ncharacteristics challenging. While PPG-based methods are invaluable, the lack of real-world datasets poses\r\na significant challenge in maximizing the benefits of PPG technology. In this paper, we introduce a dataset\r\ncomprising 1-minute fingertip video recordings from 150 anemic patients, obtained using a smartphone’s\r\ncamera. The dataset, publicly accessible for research purposes (https://forms.gle/LB4qn81ZMqEuy3V27),\r\ncovers an age range of 6 months to 32 years, with diverse hemoglobin values (4.3 gm/dL - 12.4 gm/dL).\r\nUtilizing this dataset, we propose a deep learning-based technique employing the ResNet-18 architecture to\r\nestimate hemoglobin levels. This approach eliminates the need for manual feature extraction and selection\r\nfrom PPG signals, overcoming a limitation in existing smartphone PPG-based hemoglobin estimation\r\nsystems. Our model achieves a hemoglobin level estimation with an RMSE of 0.81-1.39 when compared\r\nwith the gold standard laboratory method, Complete Blood Count (CBC) test reports. In contrast, HemaApp,\r\na state-of-the-art research utilizing a machine learning-based classifier (SVM), yields an RMSE of 1.7 on\r\nour dataset. The accuracy and simplicity of our model position it as a promising alternative to existing noninvasive hemoglobin level estimation methods.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10522589","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522589","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fingertip-video-dataset-for-non-invasive","repo_url":"https://github.com/IntelliHb/Fingertip-Video-Dataset-and-its-Deep-Learning-based-Mining-for-Hemoglobin-Estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cbc-test","task_name":"CBC TEST"},{"task_slug":"photoplethysmography-ppg","task_name":"Photoplethysmography (PPG)"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"}],"datasets_introduced":[{"slug":"fingertip-video-dataset-of-hb-estimation","name":"Fingertip Video Dataset of HB Estimation","full_name":"Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}