Author(s): Alamu Femi O, Abiodun Adeyinka O., & Jinadu Ahmad Adekunle
Malaria remains one of the major challenges faced in healthcare in Africa, especially in Nigeria with an estimated 300,000 children killed by malaria annually. Apart from low doctor-to-patient ratio in Nigeria, poor diagnosis is another major cause of increase in malaria death rate.
This research developed a Clinical Decision Support System (CDSS) to detect malaria-infected patients using Deep and Machine learning technique. For this, we developed an in-depth learning method from camera captured Giemsa-stained thin blood smear slides from 150 Plasmodium Falciparum-infected and 50 non-infected patients from a National Center for Biomedical Communications. The dataset contains 27,558 cell images with equal number of malaria-infected and non-infected cell images which are 13,779. The architecture of the proposed model predicted patient’s malaria status and was evaluated using 5-fold cross-validation. The images were preprocessed and resized after which the learning stage began. Deep learning and classification were carried out using Convolutional Neural Network (CNN). The CNN model was trained by using stochastic gradient descent (SGD) and Nesterov’s momentum to optimizing the multinomial logistic regression objective. The proposed model achieved a training accuracy of 99%, validation accuracy of 97%, 40% train loss, 35% validation loss and 98% prediction.