Hematological Image Analysis: Enhancing Blood Cell Segmentation through Advanced Image Processing Techniques

This research study intends to solve the severe problem of poikilocytosis, a disorder that causes red blood cells (RBCs) to take on aberrant forms. This system analyzes blood smear images captured with a compound microscope by using sophisticated image processing algorithms. The method's main component is the application of the Faster R-CNN algorithm. It not only recognizes different RBC forms but also classifies them, providing information about possible related illnesses. The method significantly increases the dataset over existing methods while utilizing Faster R-CNN's better efficiency. The erythrocyte and BCCD databases data combines to create this expansion, which includes a wide range of RBC forms. The speed and precision of RBC shape detection is improved for poikilocytosis diagnosis by utilising Faster R-CNN. The larger dataset provides a deeper investigation of the wide variety of RBC forms, increasing the likelihood of finding connections to different blood cell-related illnesses. The main objective of this research study is to present a thorough report that improves patient outcomes, healthcare practices by advancing the diagnosis and knowledge of blood cell-related illnesses. This initiative paves way for better patient care and diagnostic accuracy by supporting continuing efforts to improve healthcare and medical diagnostics.