A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images

Abstract

The numerous rising infections and deaths of malaria and acute lymphoblastic leukaemia (ALL) highlights the urgent need for early, useful, and efficient diagnosis methods. Recently, the framework of artificial intelligence has been applied to minimize time-consuming tasks, to increase the accuracy and flexibility of clinical diagnoses, and to reduce the pressure on physicians, diagnosticians, and clinical experts. In this study, a detection system for malaria and ALL is proposed that utilizes blood smear microscopic images with the aid of deep learning algorithms to identify and classify these two diseases automatically. The blood smear microscopic images consist of 1503 ALL images, 891 malaria images, and 1503 normal images that were divided into a training, validation, and testing sets in ratios of 50, 25, and 25%, respectively. The proposed model was built into three stages including the first stage for segmentation-applied modified UNet pre-trained model, the second stage for classification based on the convolution neural network model, and the final stage for classification utilizing perceptron as the combining model. As a result, the proposed system provides an alternative and interpretable method to detect abnormal leukocytes for ALL and malaria-infected blood cells with a 93% overall accuracy including the detection rate for ALL of 95% and the detection rate for malaria of 92%.

Keywords

• acute lymphoblastic leukaemia
• blood smear microscopic image
• deep learning
• malaria