Current - Issue

Blood group is foremost important thing for every people during an emergency situation. Verifying the person’s blood type is very significant while donation the blood to some person. Blood typing is a process to detecting the specific blood groups for the specific person. Image based representation of blood cells are broadly used to examine the cell morphology by the tool microscope in medical field to diagnosis some diseases which is related to blood. Classical procedures are usually labor-intensive and require human intervention which makes it time-consuming necessitating the development of new approaches that can deliver more efficient and reliable results. The proposed method combines PIO, a bio-inspired optimization algorithm motivated by pigeons’ efficient foraging strategies and flight navigation techniques with Otsu Method, an established image segmentation technique. By merging PIO with Otsu, this study aims to alleviate the shortcomings of conventional methods employed in detecting blood groups by providing an autonomous adaptive framework for effective segmentation. The methodology is tested using diverse data sets where the findings are compared with those obtained from traditional approaches to demonstrate superiority of the integrated approach. The results show that combining PIO with Otsu helps improve accuracy and efficiency in blood group detection significantly reducing manual work and increasing automation robustness.