Singapore is a hotspot for emerging infectious diseases and faces a constant risk of pandemic outbreaks as a major travel and health hub for Southeast Asia. With an increasing penetration of smart phone usage in this region, Singapore’s pandemic preparedness framework can be strengthened by applying a mobile-based approach to health surveillance and control, and improving upon existing ideas by addressing gaps, such as a lack of health communication. FluMob is a digitally integrated syndromic surveillance system designed to assist health authorities in obtaining real-time epidemiological and surveillance data from health-care workers (HCWs) within Singapore, by allowing them to report influenza incidence using smartphones. The system, integrating a fully responsive web-based interface and a mobile interface, is made available to HCW using various types of mobile devices and web browsers. Real-time data generated from FluMob will be complementary to current health-care- and laboratory-based systems. This paper describes the development of FluMob, as well as challenges faced in the creation of the system.

Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model’s performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.

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