Automatic Scoring of Diabetic Foot Ulcers through Deep CNN Based Feature Extraction with Low Rank Matrix Factorization Severity stage classification of diabetic foot ulcers is a vital way to improve the decision support on treatment planning and diagnostic performance. Nevertheless, the precise manual identification of ulcer severity stage is challenging since vision may vary upon the consultant, tedious and timeconsuming. Accordingly, automatic severity stage classification is of significant prominence. Yet a comprehensive computeraided Wagner scale based severity stage classification system for diabetic foot ulcers is not available in the literature. In this paper, we propose to use a convolutional neural network engineered from DenseNet201 based architecture as the feature extractor paradigm followed by a global average pooling (GAP) layer to predict sixclass severity stages of diabetic foot ulcers. Furthermore, we use a practice of optimizing processing time and memory consumption while conserving the accuracy of the classification model through feature extraction with SVD. The proposed architecture could achieve an accuracy of over 96%. Moreover, we evaluated how different pretrained CNN state of the art architectures (DenseNet, ResNet, Xception, InceptionV3, InceptionResNetV2, and VGG) which can be used for the task of transfer learning.

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).