Search results for "CNN"
showing 10 items of 36 documents
Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
2021
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then d…
Il neonato che “sa di sale”
2021
Pseudohypoaldosteronism type 1 (PHA1) is a rare genetic disease due to the peripheral resistance to aldosterone. Clinical spectrum with neonatal onset includes salt loss, hyponatremia, hypochloraemia, hyperkalaemia, metabolic acidosis and increased plasmatic levels of aldosterone. Two forms of the disease - renal and systemic – have been described, which are genetically distinct and with wide clinical expressivity. The most severe generalized PHA1 is caused by mutations in the genes encoding for the subunits of the epithelial sodium channels (ENaC). The paper reports the case of a newborn of the first pregnancy of healthy and consanguineous Sicilian parents, with a clinical and hormonal pic…
Deep learning models for bacteria taxonomic classification of metagenomic data.
2018
Background An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions.…
Development of handcrafted and deep based methods for face and facial expression recognition
2021
The research objectives of this thesis concern the development of new concepts for image segmentation and region classification for image analysis. This involves implementing new descriptors, whether color, texture, or shape, to characterize regions and propose new deep learning architectures for the various applications linked to facial analysis. We restrict our focus on face recognition and person-independent facial expressions classification tasks, which are more challenging, especially in unconstrained environments. Our thesis lead to the proposal of many contributions related to facial analysis based on handcrafted and deep architecture.We contributed to face recognition by an effectiv…
HEp-2 intensity classification based on deep fine-tuning
2020
The classification of HEp-2 images, conducted through Indirect ImmunoFluorescence (IIF) gold standard method, in the positive / negative classes, is the first step in the diagnosis of autoimmune diseases. Since the test is often difficult to interpret, the research world has been looking for technological features for this problem. In recent years the methods of deep learning have overcome the other machine learning techniques in their effectiveness and robustness, and now they prevail in artificial intelligence studies. In this context, CNNs have played a significant role especially in the biomedical field. In this work we analysed the capabilities of CNN for fluorescence classification of…
Automated Diagnostics of Retinal Pathologies Using OCT Volumes
2020
The leading cause of blindness in the population could mostly be the degeneration of the retina caused by the diabetic-related problems and the aging issue. Diabetic retinopathy (DR) and diabetic macular edema (DME) are the main direct causes of vision problems in the labor age citizens of most advanced countries. The elevated number of diabetic people globally indicates that DME and DR will remain to be the principal factor to partial or total vision loss, which affects the lives quality of patients for many years to come and threaten their lives. Therefore, early detection followed by fast treatment procedures of persons with diabetic-related diseases is significant in preventing optical …
Rethinking the sGLOH Descriptor
2018
sGLOH (shifting GLOH) is a histogram-based keypoint descriptor that can be associated to multiple quantized rotations of the keypoint patch without any recomputation. This property can be exploited to define the best distance between two descriptor vectors, thus avoiding computing the dominant orientation. In addition, sGLOH can reject incongruous correspondences by adding a global constraint on the rotations either as an a priori knowledge or based on the data. This paper thoroughly reconsiders sGLOH and improves it in terms of robustness, speed and descriptor dimension. The revised sGLOH embeds more quantized rotations, thus yielding more correct matches. A novel fast matching scheme is a…
Deep CNN for IIF Images Classification in Autoimmune Diagnostics
2019
The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The class…
Deep Convolutional Neural Network for HEp-2 fluorescence intensity classification
2019
Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the …
Iegultā intelekta risinājums heterogenā iegultā sistēmā objektu detektēšanai attēlos
2020
Maģistra darba mērķis ir veikt iegultā intelekta risinājumu izpēti un eksperimentāla risinājumaimplementēšanu heterogenā iegultā sistēmā objektu detektēšanai attēlos.Darbā teorētiski ir aprakstīti heterogēnu iegulto iekārtu un konvolūciju neironu tīklu dar-bības pamatprincipi. Tāpat darbā tiek apskatīti daži populārākie konvolūciju neironu tīkli, kasparedzēti objektu detektēšanai attēlos. Tiek skaidrota to uzbūve un novērtēšanas metodes. Darbapraktiskajā daļā tiek aprakstīta divu dažādu konvolūciju neironu tīklu apmācīšana un uzstādīšanauz heterogēnas iegultās iekārtas, no kuriem viens ir paredzēts attēlu klasificēšanai, bet otrsobjektu detektēšanai attēlos.