Mcguirepilegaard1105

Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.3D data is becoming increasingly popular and accessible for computer vision tasks. A popular format for 3D data is the mesh format, which can depict a 3D surface accurately and cost-effectively by connecting points in the (x, y, z) plane, known as vertices, into triangles that can be combined to approximate geometrical surfaces. However, mesh objects are not suitable for standard deep learning techniques due to their non-euclidean structure. We present an algorithm which predicts the sex, age, and body mass index of a subject based on a 3D scan of their face and neck. This algorithm relies on an automatic pre-processing technique, which renders and captures the 3D scan from eight different angles around the x-axis in the form of 2D images and depth maps. Subsequently, the generated data is used to train three convolutional neural networks, each with a ResNet18 architecture, to learn a mapping between the set of 16 images per subject (eight 2D images and eight depth maps from different angles) and their demographics. For age and body mass index, we achieved a mean absolute error of 7.77 years and 4.04 kg/m2 on the respective test sets, while Pearson correlation coefficients of 0.76 and 0.80 were obtained, respectively. The prediction of sex yielded an accuracy of 93%. The developed framework serves as a proof of concept for prediction of more clinically relevant variables based on 3D craniofacial scans stored in mesh objects.Cervical cancer is the fourth most common cancer among women and still one of the major causes of women's death around the world. Early screening of high grade Cervical Intraepithelial Neoplasia (CIN), precursors to cervical cancer, is vital to efforts aimed at improving survival rate and eventually eliminating cervical cancer. Visual Inspection with Acetic acid (VIA) is an assessment method which can inspect the cervix and potentially detect lesions caused by human papillomavirus (HPV), which is a major cause of cervical cancer. VIA has the potential to be an effective screening method in low resource settings when triaged with HPV test, but it has the drawback that it depends on the subjective evaluation of health workers with varying levels of training. A new deep learning algorithm called Automated Visual Evaluation (AVE) for analyzing cervigram images has been recently reported that can automatically detect cervical precancer better than human experts. In this paper, we address the question of whether mobile phone-based cervical cancer screening is feasible. read more We consider the capabilities of two key components of a mobile phone platform for cervical cancer screening (1) the core AVE algorithm and (2) an image quality algorithm. We consider both accuracy and speed in our assessment. We show that the core AVE algorithm, by refactoring to a new deep learning detection framework, can run in ~30 seconds on a low-end smartphone (i.e. Samsung J8), with equivalent accuracy. We developed an image quality algorithm that can localize the cervix and assess image quality in ~1 second on a low-end smartphone, achieving an area under the ROC curve (AUC) of 0.95. Field validation of the mobile phone platform for cervical cancer screening is in progress.In this paper, we consider the problem of classifying skin lesions into multiple classes using both dermoscopic and clinical images. Different convolutional neural network architectures are considered for this task and a novel ensemble scheme is proposed, which makes use of a progressive transfer learning strategy. The proposed approach is tested over a dataset of 4000 images containing both dermoscopic and clinical examples and it is shown to achieve an average specificity of 93.3% and an average sensitivity of 79.9% in discriminating skin lesions belonging to four different classes.Urolithiasis is a common disease around the world and its incidence has been growing every year. There are various diagnosis techniques based on kidney stone identification aiming to find the formation cause. However, most of them are time consuming, tedious and expensive. The accuracy of the diagnosis is crucial for the prescription of an appropriate treatment that can eliminate the stones and diminish future relapses. This paper presents two effective supervised learning methods to automate and improve the accuracy of the classification of kidney stones; as well as a dataset consisting of kidney stone images captured with ureteroscopes. In the proposed methods, the image features that are visually exploited by urologists to distinguish the type of kidney stones are analyzed and encoded as vectors. Then, the classification is performed on these feature vectors through Random Forest and ensemble K Nearest Neighbor classifiers. The overall classification accuracy obtained was 89%, outperforming previous methods by more than 10%. The details of the classifier implementation, as well as their performance and accuracy, are presented and discussed. Finally, future work and improvements are proposed.Anemia is a disease present worldwide. High prevalence of anemia (43%) is found in the child population and its main long-term effect (slow cognitive development) can remain even if the disease has disappeared. One of the main reasons for the high prevalence of anemia in Peru is the poor screening coverage during the growth of the child due to the parents' fear of infringing pain on their children. We take advantage that anemia produces pallor in the hands, fingers and ungueal bed to develop a semaphore for this disease. This screening tool uses photographic images of the patient's ungueal bed to determine if they have a high, medium or low possibility of having anemia. Sixty people participated in the study and 6 photographic images for each participant's right hand were captured. The images were processed to extract regions of interest from each of the fingernails. Datasets were generated and a neural network was used to predict the risk of anemia. Initial results show that the proposed semaphore of anemia reaches a sensitivity of 0.