Aryan Verma

Work place: School of Mathematics, University of Edinburgh, UK

E-mail: A.Verma-14@sms.ed.ac.uk

Website:

Research Interests: Theoretical Computer Science, Deep Learning, Data Science, Machine Learning

Biography

Mr. Aryan Verma is currently a post graduate student at School of Mathematics, University of Edinburgh, UK. He did his B. Tech in Computer Science and Engineering from National Institute of Technology Hamirpur, India. His research interest includes Social Data Science, Deep Neural Network, Statistical Machine Learning and Computer Vision. He has published several papers in high indexed journals and international conferences and attended various fellowships including prestigious Data Science for Social Good fellowship at University of Warwick UK.

Author Articles
Non-Invasive Blood Group Prediction Using Optimized EfficientNet Architecture: A Systematic Approach

By Nitin Sakharam Ujgare Nagendra Pratap Singh Prem Kumari Verma Madhusudan Patil Aryan Verma

DOI: https://doi.org/10.5815/ijigsp.2024.01.06, Pub. Date: 8 Feb. 2024

This research work proposed a non-invasive blood group prediction approach using deep learning. The ability to swiftly and accurately determine blood types plays a critical role in medical emergencies prior to administering red blood cell, platelet, and plasma transfusions. Even a minor error during blood transfer can have severe consequences, including fatality. Traditional methods rely on time-consuming automated blood analyzers for pathological assessment. However, these processes involve skin pricking, which can cause bleeding, fainting, and potential skin lacerations. The proposed approach circumvents noninvasive procedures by leveraging rich EfficientNet deep learning architecture to analyze images of superficial blood vessels found on the finger. By illuminating the finger with laser light, the optical image of blood vessels hidden on the finger skin surface area is captured, which incorporates specific antigen shapes such as antigen ‘A’ and antigen ‘B’ present on the surface. Captured shapes of different antigen further used to predict the blood group of humans. The system requires high-definition camera to capture the antigen pattern from the red blood cells surface for classification of blood type without piercing the skin of patient. The proposed solution is not only straightforward and easily implementable but also offers significant advantages in terms of cost-effectiveness and immediate identification of ABO blood groups. This approach holds great promise for medical emergencies, military battleground scenarios, and is particularly valuable when dealing with infants where invasive procedures pose additional risks.

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