Frequency Domain Convolutional Neural Network: Accelerated CNN for Large Diabetic Retinopathy Image Classification

The conventional spatial convolution layers in the Convolutional Neural Networks (CNNs) are computationally expensive at the point where the training time could take days unless the number of layers, the number of training images or the size of the training images are reduced. The image size of 256x256 pixels is commonly used for most of the applications of CNN, but this image size is too small for applications like Diabetic Retinopathy (DR) classification where the image details are important for accurate classification. This research proposed Frequency Domain Convolution (FDC) and Frequency Domain Pooling (FDP) layers which were built with RFFT, kernel initialization strategy, convolution artifact removal and Channel Independent Convolution (CIC) to replace the conventional convolution and pooling layers. The FDC and FDP layers are used to build a Frequency Domain Convolutional Neural Network (FDCNN) to accelerate the training of large images for DR classification. The Full FDC layer is an extension of the FDC layer to allow direct use in conventional CNNs, it is also used to modify the VGG16 architecture. FDCNN is shown to be at least 54.21% faster and 70.74% more memory efficient compared to an equivalent CNN architecture. The modified VGG16 architecture with Full FDC layer is reported to achieve a shorter training time and a higher accuracy at 95.63% compared to the original VGG16 architecture for DR classification.

A Review of Machine Learning Techniques for Applied Eye Fundus and Tongue Digital Image Processing with Diabetes Management System

Diabetes is a global epidemic and it is increasing at an alarming rate. The International Diabetes Federation (IDF) projected that the total number of people with diabetes globally may increase by 48%, from 425 million (year 2017) to 629 million (year 2045). Moreover, diabetes had caused millions of deaths and the number is increasing drastically. Therefore, this paper addresses the background of diabetes and its complications. In addition, this paper investigates innovative applications and past researches in the areas of diabetes management system with applied eye fundus and tongue digital images. Different types of existing applied eye fundus and tongue digital image processing with diabetes management systems in the market and state-of-the-art machine learning techniques from previous literature have been reviewed. The implication of this paper is to have an overview in diabetic research and what new machine learning techniques can be proposed in solving this global epidemic.