Convolutional neural network (CNN)-based methods have achieved state-of-the-art performance in solving several complex computer vision problems including assessment of diabetic retinopathy (DR). Despite this, CNN-based methods are often criticized as “black box” methods for providing limited to no understanding about their internal functioning. In recent years there has been an increased interest to develop explainable deep learning models, and this paper is an effort in that direction in the context of DR. Based on one of the best performing method called Grad-CAM++, we propose Advanced Grad-CAM++ to provide further improvement in visual explanations of CNN model predictions (when compared to Grad-CAM++), in terms of better localization of DR pathology as well as explaining occurrences of multiple DR pathology types in a fundus image. By keeping all the layers and operations as is, the proposed method adds an additional non-learnable bilateral convolutional layer between the input image and the very first learnable convolutional layer of Grad-CAM++. Experiments were conducted on fundus images collected from publicly available sources namely EyePACS and DIARETDB1. Intersection over Union (IoU) score between the ground truth and heatmap produced by the methods were used to quantitatively compare the performance.The overall IoU score for Advanced Grad-CAM++ is 0.179, whereas for Grad-CAM++ it is score 0.161. Thus an 11.18% improvement in agreement with the ground truths by the proposed method is inferable.