# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
###### Abstract
[7] is not necessary because the classification of the ROI based on a single feature (i.e., the standard deviation) can be easily performed using a single threshold value. Finally, the approach presented by [PERSON] et al. [8] requires a special environment and configuration for the blood slide, the light intensity, and angle at the time of photo capture because it depends on a static fixed value for the luminance. Researchers in [9] detect the contours in the input image and identify the agglutination based on the number of components, where a threshold of 5 is considered for the connected components. However, this technique may result in false blobs in the background. Other approaches adopted electrical circuits to perform automatic blood typing. According to the approach in [2], a light is generated by an LED and passed through blood samples using optical fiber cables. A specific diode is used as a photo detector. The approach discriminates between different blood groups according to voltage variations from the photo detector. A similar approach was proposed in [3] based on an infrared (IR) light source. The blood sample is located between the IR transmitter and receiver. The blood type is determined according to the intensity of the received IR light. [PERSON] et al. [10] propose a portable device for blood typing by identifying the spectral differences between agglutinated and nonagglutinated samples, where the result requires up to 5 minutes to be ready. The device was tested using 50 blood samples. A hardware implementation for blood typing system is presented by [PERSON] et al. [11] using a Raspberry Pi single-board computer. The system detects the contours in the blood spot and determines the existence of the agglutination if the number of the detected contours exceeds a given threshold. A total of 75 blood samples were used in system construction and evaluation. These approaches require additional special hardware. Regardless of the additional cost, the hardware may not be available everywhere or every time. Many other techniques can be used to classify medical images into normal and infected (i.e., with or without agglutination, in our case). Frequency domain analysis was used by [PERSON] et al. [12] to classify nanometer-sized objects in images provided by a biosensor. Fourier and wavelet features are extracted from the input images and analyzed where the decision tree and random forest are used for classification. [PERSON] et al. [13] proposed a classification method based on the wavelet transformation for feature extraction. The classification is performed using an interpolation scheme. The wavelet decomposition was further improved by proposing an adaptive framework to improve the performance in terms of down sampling balance and signal compression [14]. The wavelet was also used by [PERSON] et al. [15] to reduce the haze and enhance texture details of the images in frequency domain. Wavelet features can be represented using sophisticated mathematical models such as fractal descriptors [16; 17; 18]. However, these techniques require domain-dependent knowledge in case of model updating or scaling. Although the blood type determination based on feature engineering (i.e., extracting features depending on domain knowledge) is still the most prevalent in research studies, other studies used image matching algorithms such as scale invariant feature transform (SIFT) and speed-up robust feature (SURF). For example, SIFT was used in [19] to transform the green component of the image into a collection of local feature vectors, after many preprocessing techniques such as thresholding and morphological operations. Then, the SVM algorithm is used for classification. The proposed approach was evaluated using only 30 blood samples. Furthermore, [PERSON] and [PERSON] [20] employed the SURF algorithm to detect the agglutination in blood using only 84 blood samples. However, SIFT and SURF algorithms are quite slow compared with other newer image matching algorithms such as oriented fast and rotated brief (ORB) [21].