An Approach Towards Automatic Detection of Toxoplasmosis using Fundus Images Ocular Toxoplasmosis (OT) is a widespread infectious chorioretinal disease whose timely diagnosis and treatment are crucial to prevent potential vision loss. Diagnosing OT is a challenging task ranging from tedious analyses of fundus images of the eye to serological clinical tests. An automated approach using convolutional neural networks (CNNs) towards diagnosing OT by analyzing fundus images is described. Fundus images are segmented to patches using a sliding window and are classified into healthy and unhealthy fundus image patches using a CNN model. An OT lesion heat map of a fundus image is generated from these patches. The heat map and patch features are then combined to develop a dual input hybrid CNN model detecting OT fundus images with high accuracy. The approach was applied to a dataset of fundus images involving OT and normal subjects and was highly effective in identifying fundus images having OT lesions.

Automated Grading of Diabetic Retinopathy in Retinal Fundus Images using Deep Learning The importance of early detection of Diabetic Retinopathy (DR) and the time consuming and error prone nature of manual screening of the disease necessitates an automated system that can identify and grade the disease reliably. In this paper, we propose a deep learning solution for automated grading of DR from retinal fundus images. Specifically, we employ Convolutional Neural Network (CNN) architectures which have achieved stateoftheart performance in image recognition tasks. The experimental results demonstrate that the best performing deep learning model achieves a high sensitivity of 94.3% and specificity of 95.5%(for DRnoDR classification) and a quadratic weighted kappa score  (kappa2)  of 0.88 (for grading from 0 through 4), indicating an improved  kappa2  score compared to the stateoftheart method ( kappa2  score of 0.81).