Optimisation of Erythrocyte Abnormality Classification using Watershed Segmentation - Parahita, Syavina Octavia; Fitri, Zilvanhisna Emka; Imron, Arizal Mujibtamala Nanda (Algoritme Jurnal Mahasiswa Teknik Informatika, 2025)
According to the World Health Organization (WHO), Polycythemia vera (PV) belongs to one of the main categories of Myeloproliferative Neoplasm (MPN). The results of laboratory diagnosis of PV are characterized by an increase in the number of erythrocytes, hemoglobin, leukocytes and platelets. Generally, blood examination uses automatic full blood count (FBC), but this method cannot detect abnormalities in the shape of erythrocytes, so further processing is needed from microscopic examination by creating a system that is able to detect and identify red blood cell abnormalities automatically. The system is a combination of digital image processing methods and intelligent systems methods commonly known as computer vision. The watershed segmentation method is used to separate closely packed cells, while the backpropagation method is an intelligent system capable of classifying erythrocyte shape abnormalities. The amount of data used is 340 training data and 50 test data, while the most optimal learning rate is 0.6 with a maximum epoh of 100 so that the system accuracy is 88%, specificity is 0.056 and sensitivity is 0.714.