A Novel Deep Learning Approach to Malaria Disease Detection on Two Malaria Datasets

Abstract

Malaria is a contagious febrile disease transmitted to humans by the bite of female mosquitoes. It is important to diagnose this disease in a short period of time. Finding the mathematically best numerical solution to a particular problem is the most important issue for most departments. In deep learning-based systems developed, the difference between the real data and the predicted result of the model is measured using loss functions. To minimize the error rate in the predictions during the training process of deep learning models, the weight values used in the model should be updated. This update process has a significant effect on the model prediction result. This article presents a new deep learning-based malaria detection method that will help diagnose malaria in a short time. A new 21-layer Convolutional Neural Network (CNN) model is designed and proposed to describe infected and uninfected thin red blood cell images. By using thin red blood cell sample images, 95% accuracy was achieved with Nadam and RMSprop optimization techniques. The results obtained show the efficiency of the proposed method according to each optimization algorithm.