AUTOMATED DETECTION OF MALARIAL RETINOPATHY USING TRANSFER LEARNING

Abstract

Cerebral Malaria (CM) is a severe neurological syndrome of malaria mainly found in children and is associated with highly specific retinal lesions. The manifestation of these indications of CM in the retina is called malarial retinopathy (MR). All patients showing clinical signs of CM are commonly diagnosed and treated accordingly; however, 23% of them are misdiagnosed as they suffer from another infection with identical clinical symptoms. Due to these underlying symptoms, the false positive cases may go untreated and could result in death of the patients. A diagnostic test is needed that is highly specific in order to reduce false positives. The purpose of this study to demonstrate a technique based on a transfer learning technique using images from three different retinal cameras to identify the hemorrhages and whitening lesions in the retina which can accurately identify the patients with MR. The MR detection model gives a specificity of 100% and a sensitivity of 90% with an AUC of 0.98. The algorithm demonstrates the potential of accurate MR detection with a low-cost retinal camera.

Keywords: Cerebral malaria (CM); Convolutional neural network (CNN); Data Augmentation; Malarial Retinopathy (MR); Transfer learning.

Figure 1.

Sample images from the Topcon camera showing the two principal lesions: hemorrhages (a), whitening (b) and an image with no lesions (c).

Figure 2.

Block diagram of the proposed method.

Figure 3:

Patient-based ROC curve (a) using only Pictor-Plus images for training and testing. (b) using Topcon, Pictor-Plus and iNview images for training and only iNview images for testing.