Blood Grouping Detection Using Image Processing and Deep Learning
There is worldwide demand for an affordable Blood Group measurement solution, which is a particularly urgent need in developing countries. The Image Processing, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. This Project proposes a noninvasive Blood Group measurement processes. Also its compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, Image Processing algorithms, and Detection models to calculate Blood Groups. This analysis was then used to recommend realistic approaches to build a Image Processing-based point-of-care tool for Blood Group measurement in a noninvasive manner. In this project, proposing approaches for blood Group measurement with the aim of recommending data collection techniques, signal extraction processes, feature calculation processes, Image Processing algorithms for developing a noninvasive Blood Group estimation using a Image.
Blood Group level measurement is a blood diagnosis process to determine the concentration of cell count in the blood. Clinicians measure Blood Group in several ways.
Although the invasive (blood sample collection) approach remains the most common.
Invasive processes involve the addition of various chemicals to a blood sample and then optical variations are calculated using spectroscopic data to measure the Blood Group level.
(1) challenging data collection methods
(2) complex data analysis and feature extraction processes
(3) lack of affordability and portability
(4) lack of user-friendliness with costly external modules.
A noninvasive (without blood sample collection) approach involves data obtained from image sensors, spectroscopic information, and output of a photoplethysmographic (PPG) sensor to calculate the Blood Group level. An Image Processing-based POC tool as a potential alternative to invasive clinical blood testing is rapidly attracting attention because of the advantages of availability, user-friendliness, and easy attachability to different biosensing devices. The fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light were identified as potential light sources to receive a Blood Group response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. The PPG signal is generated from each video, and multiple characteristic features are then extracted from the PPG signal, its derivatives and from Frequency analysis. genetic algorithms (GA) has been used to select the optimal features (Feature selection). Finally, CNN based models have been developed to estimate the blood Blood Group (Blood Group) levels from the selected features. The approach expected to provides the best-estimated accuracy of aound 98%.
- Front End – Anaconda IDE
- Backend – SQL
- Language – Python 3.8
- Hard Disk: Greater than 500 GB
- •RAM: Greater than 4 GB
- •Processor: I3 and Above
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