Feature Edge Extraction for Human Body Cut Injured Detection using Deep Learning - Chandrashekhar Kumar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In recent endeavours, the management style of medical patient treatment has been growing continuously. In the meantime, trends demand more accuracy and work performance beyond the level of advancement for waiting in line in hospitals, ending with emergency cases as well. To overcome the delay in response from the hospital end with medical high demand, the doctor-patient ratio is also around 1:700. And the need for blood in a human case is a little crucial based on the blood group. Also, to avoid this scenario and save human lives, we have to implement blood detection on the spot using the image processing technique with deep learning, which can close the gap between the arrangement of the blood bank system and human blood safety support with the patient as well. The current study can only be done after reaching the hospital; using various research attempts to fill this gap. But delay is still needed to reach proper support with advanced technology. To extract the image of blood colour detection using an optical clamp-on Sensor(OCS) deep learning blood detection and amount of blood flow based on healthy body range of blood status with a range of 5 litters for the adult body with advanced BMI technology.