Classification of Malaria cell images using Deep Learning Approach

Malaria is a disease caused by the bite of infected female Anopheles mosquitos. Symptoms of malaria are fever, vomiting, headache and in extreme cases, it may lead to death. In this research paper, we used patients' red blood cell images and deep learning techniques to build a machine-learning model for blood smear image classification. CNN (Convolutional Neural Network) is one of the deep learning methods which is used for image classification. The previous related works mostly used Support Vector Machine (SVM) for smear image classification which is quite complex. The pre-trained CNN extracts the infected and uninfected blood cell images. In this proposed method, we used deep learning combined with VGG to perform the classification of parasitized and uninfected blood smear cell images. To validate our proposed method, we used datasets from the NIH repository which consists of parasitized and uninfected cell images. In this research, our proposed approach achieved an accuracy of 96.02%.