Pendeteksi Golongan Darah Manusia Berbasis Tensorflow menggunakan ESP32-CAM

ABSTRAKPendeteksian golongan darah dilakukan untuk mengetahui golongan darah yang dimiliki. Hingga saat ini pendeteksian golongan darah masih dilakukan oleh petugas analis kesehatan menggunakan kemampuan mata manusia. Pada penelitian ini dilakukan perancangan alat pendeteksi golongan darah menggunakan ESP32-CAM. Alat ini menggunakan kamera OV2640 untuk menangkap citra, yang diproses menggunakan Tensorflow Object Detection API sebagai framework untuk melatih serta mengolah citra darah. Model latih akan digunakan pada kondisi pendeteksian langsung dan ditampilkan dalam bentuk jendela program golongan darah beserta tingkat akurasinya. Dalam penelitian ini pengujian dilakukan menggunakan 20 dataset dengan jarak pengukuran antara ESP32-CAM dengan citra golongan darah yaitu sejauh 20 cm. Hasil yang didapat selama pengujian mayoritas golongan darah yang dapat terdeteksi adalah golongan darah AB.Kata kunci: ESP32-CAM, Tensorflow, Python, Golongan Darah, Pengolahan Citra ABSTRACTBlood group detection is performed to determine the blood group. Currently, in detecting blood type, it still relies on the ability of the human eyeThis paper presents a human blood group detection device using ESP32-CAM. This tool uses ESP32-CAM to capture images, and the Tensorflow Object Detection API as a framework used to train and process an image. The way this tool works is that the ESP32-CAM will capture an image of the blood sample and then send it via the IP address. Through the IP Address, the python program will access the image, then the image will be processed based on a model that has been previously trained. The results of this processing will be displayed in the form of a window program along with the blood type and level of accuracy. In this study, testing was carried out based on the number of image samples, the number of datasets, and the measurement distance. The ideal measurement distance between the ESP32-CAM and the blood group image is 20 cm long. The results obtained during the testing of the majority of blood groups that can be detected are AB blood group.Keywords: ESP32-CAM, Tensorflow, Python, Blood Type, Image Processing

BGvar - a comprehensive resource for blood group immunogenetics

BackgroundBlood groups form the basis of effective and safe blood transfusion. There are about 41 well recognized human blood group systems presently known. Blood groups are molecularly determined by the presence of specific antigens on the red blood cells and are genetically determined and inherited following Mendelian principles. The lack of a comprehensive, relevant, manually compiled and genome-ready dataset of red cell antigens limited the widespread application of genomic technologies to characterise and interpret the blood group complement of an individual from genomic datasets.Materials and MethodsA range of public datasets were used to systematically annotate the variation compendium for its functionality and allele frequencies across global populations. Details on phenotype or relevant clinical importance were collated from reported literature evidence.ResultsWe have compiled the Blood Group Associated Genomic Variant Resource (BGvar), a manually curated online resource comprising all known human blood group related allelic variants including a total of 1672 ISBT approved alleles and 1552 alleles predicted and curated from literature reports. This repository includes 1606 Single Nucleotide Variations (SNVs), 270 Insertions, Deletions (InDels) and Duplications and about 1310 combination mutations corresponding to 41 human blood group systems and 2 transcription factors. This compendium also encompasses gene fusion and rearrangement events occurring in human blood group genes.ConclusionTo the best of our knowledge, BGvar is a comprehensive and a user friendly resource with most relevant collation of blood group alleles in humans. BGvar is accessible online at URL: http://clingen.igib.res.in/bgvar/