Abstract
Microscopic examination is currently the gold standard test for diagnosis of several neglected tropical diseases. However, reliable identification of parasitic infections requires in-depth train and access to proper equipment for subsequent microscopic analysis. These requirements are closely related with the increasing interest in the development of computer-aided diagnosis systems, and Mobile Health is starting to play an important role when it comes to health in Africa, allowing for distributed solutions that provide access to complex diagnosis even in rural areas. In this paper, we present a 3D-printed microscope that can easily be attached to a wide range of mobile devices models. To the best of our knowledge, this is the first proposed smartphone-based alternative to conventional microscopy that allows autonomous acquisition of a pre-defined number of images at 1000x magnification with suitable resolution, by using a motorized automated stage fully powered and controlled by a smartphone, without the need of manual focus of the smear slide. Reference smears slides with different parasites were used to test the device. The acquired images showed that was possible to visually detect those agents, which clearly illustrate the potential that this device can have, specially in developing countries with limited access to healthcare services.

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This study explored the viability of out-the-box, pre-trained ConvNet models as a tool to generate features for large-scale classification tasks. A juxtaposition with generative methods for vocabulary generation was drawn. Both methods were chosen in an attempt to integrate other datasets (transfer learning) and unlabelled data, respectively. Both methods were used together, studying the viability of a ConvNet model to estimate category labels of unlabelled images. All experiments pertaining to this study were carried out over a two-class set, later expanded into a 5-category dataset. The pre-trained models used were obtained from the Caffe Model Zoo. The study showed that the pre-trained model achieved best results for the binary dataset, with an accuracy of 0.945. However, for the 5-class dataset, generative vocabularies outperformed the ConvNet (0.91 vs. 0.861). Furthermore, when replacing labelled images with unlabelled ones during training, acceptable accuracy scores were obtained (as high as 0.903). Additionally, it was observed that linear kernels perform particularly well when utilized with generative models. This was especially relevant when compared to ConvNets, which require days of training even when utilizing multiple GPUs for computations.

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Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task. The best solutions are, in general, computationally heavy preventing their use in real-time applications. This research addresses this problem by proposing a robust and computationally efficient method, BMOG, that significantly boosts the performance of the widely used MOG2 method. The complexity of BMOG is kept low, proving its suitability for real-time applications. The proposed solution explores a novel classification mechanism that combines color space discrimination capabilities with hysteresis and a dynamic learning rate for background model update.