Pemodelan Sistem Deteksi Parasit Malaria pada Citra Mikroskopis Sel Darah Menggunakan Metode Deep Learning - Nurhaeni Nurhaeni; Septyan Eka Prastya; Ahmad Hidayat; Fadhiyah Noor Anisa (LPPM STIKI MALANG, 2024)
Microscopic examination is the most common malaria examination technique used in health facilities. However, microscopic examination requires special skills and quite a long time. This research aims to develop a malaria parasite detection system model in blood cell images using deep learning technology to increase the accuracy and speed of detection with the Convolutional Neural Network (CNN) algorithm. This research was carried out in several stages: data collection, image preprocessing, dividing training data and validation data, creating a model using CNN, and evaluating the model. A CNN model was created to classify blood cell images into two classes, namely infected and uninfected. The dataset used as a reference in forming a detection system model uses blood cell images from the open-source Kaggle as many as 11.312 images. The CNN model evaluation results obtained an accuracy value of 97.17% in detecting blood cell images. These results show that the CNN model created can be used to detect malaria parasites using blood cell images.