In self-service supermarket and retail industry, efforts to reduce customer wait time using automatic grocery item identification are challenged by low recognition accuracy, long response time and substantial requirement for equipment. In this paper, we propose a novel edge computing system named EdgeVegfru for vegetable and fruit image classification. While existing work on Vegfru dataset shows excellent performance, few of them have been deployed in real-world applications. We adopt an edge computing paradigm, design, implement and evaluate the whole system on the Android devices. The proposed deep learning model and quantization algorithm reduce the model size and inference time significantly. Our system has shown out-standing accuracy within limited time and computation resources, compared with other machine learning methods(such as Support Vector Machine(SVM), Random Forest(RF)), thus providing the potential path for automatic recognition and pricing in self-service retail stores.

Diabetic retinopathy (DR) is a vision-threatening complication among the diabetic population and a leading cause of blindness for working-age adults. Early detection and timely treatment can reduce the occurrence of blindness due to DR. Computer-aided diagnosis have great potential to significantly improve the accuracy and speed in DR detection over the traditional manual diagnosis process. In this paper, we present a deep convolutional neural network for DR stage classification, trained and evaluated on a large dataset. Our model uses high-resolution retinal fundus images of both the left and right eyes as inputs to take advantage of more detailed retinal lesion information in images and strong correlation between both eyes. Selective data sampling (SeS) is applied in the training process to mitigate the data imbalance problem. Experiments show that our model outperforms the fine-tuned Inception-v3 model by every measure, achieving an accuracy of 87.2% and a Kappa score of 0.806 on the Kaggle dataset.

Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions. However, most of the existing dietary assessment methods rely on memory. With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment. However, enabling this future Internet of Things-based dietary assessment imposes several fundamental challenges on algorithm development and system design. In this paper, we set to address these issues from the following two aspects: (1) to develop novel deep learning-based visual food recognition algorithms to achieve the best-in-class recognition accuracy; (2) to design a food recognition system employing edge computing-based service computing paradigm to overcome some inherent problems of traditional mobile cloud computing paradigm, such as unacceptable system latency and low battery life of mobile devices. We have conducted extensive experiments with real-world data. Our results have shown that the proposed system achieved three objectives: (1) outperforming existing work in terms of food recognition accuracy; (2) reducing response time that is equivalent to the minimum of the existing approaches; and (3) lowering energy consumption which is close to the minimum of the state-of-the-art.

In Low and Middle-Income Countries (LMICs), efforts to eliminate the Tuberculosis (TB) epidemic are challenged by the persistent social inequalities in health, the limited number of local healthcare professionals, and the weak healthcare infrastructure found in resource-poor settings. The modern development of computer techniques has accelerated the TB diagnosis process. In this paper, we propose a novel method using Convolutional Neural Network(CNN) to deal with unbalanced, less-category X-ray images. Our method improves the accuracy for classifying multiple TB manifestations by a large margin. We explore the effectiveness and efficiency of shuffle sampling with cross-validation in training the network and find its outstanding effect in medical images classification. We achieve an 85.68% classification accuracy in a large TB image dataset, surpassing any state-of-art classification accuracy in this area. Our methods and results show a promising path for more accurate and faster TB diagnosis in LMICs healthcare facilities.