Search results for "convolutional"
showing 10 items of 186 documents
Infantile Hemangioma Detection using Deep Learning
2020
Infantile hemangiomas are the most common type of benign tumor which appear in the first weeks of life. As currently there is no robust protocol to monitor and assess the hemangioma status, this study proposes a preliminary method to detect the lesion. Therefore, in this paper we describe a hemangiomas classifier based on a linear convolutional neural network architecture. The challenge was to achieve a good classification using a relatively small internal database of 240 images from 40 different patients. The results are promising as the CNN performance evaluation showed a level of accuracy on the test set of 93.84%. Five metrics were calculated to assess the proposed model performances: a…
Proposition of Convolutional Neural Network Based System for Skin Cancer Detection
2019
Skin cancer automated diagnosis tools play a vital role in timely screening, helping dermatologists focus on melanoma cases. Best arts on automated melanoma screening use deep learning-based approaches, especially deep convolutional neural networks (CNN) to improve performances. Because of the large number of parameters that could be involved during training in CNN many training samples are needed to avoid overfitting problem. Gabor filtering can efficiently extract spatial information including edges and textures, which may reduce the features extraction burden to CNN. In this paper, we proposed a Gabor Convolutional Network (GCN) model to improve the performance of automated diagnosis of …
Combination Of Handcrafted And Deep Learning-Based Features For 3d Mesh Quality Assessment
2020
We propose in this paper a novel objective method to evaluate the perceived visual quality of 3D meshes. The proposed method in no-reference, it relies only on the distorted mesh for the quality estimation. It is based on a pre-trained convolutional neural network (i.e VGG to extract features from the distorted mesh) and handcrafted features extracted directly from the 3D mesh (i.e curvature and dihedral angle). A General Regression Neural Network (GRNN) is used to learn the statistical parameters of the feature vectors and estimate the quality score. Experimental results from for subjective databases (LIRIS masking, LIRIS/EPFL generalpurpose, UWB compression and LEETA simplification) and c…
No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling
2020
Abstract Blind or No reference quality evaluation is a challenging issue since it is done without access to the original content. In this work, we propose a method based on deep learning for the mesh visual quality assessment without reference. For a given 3D model, we first compute its mesh saliency. Then, we extract views from the 3D mesh and the corresponding mesh saliency. After that, the views are split into small patches that are filtered using a saliency threshold. Only the salient patches are selected and used as input data. After that, three pre-trained deep convolutional neural networks are employed for feature learning: VGG, AlexNet, and ResNet. Each network is fine-tuned and pro…
Deep Learning for Resource-Limited Devices
2020
In recent years, deep neural networks have revolutionized the development of intelligent systems and applications in many areas. Despite their numerous advantages and potentials, these intelligent models still suffer from several issues. Among them, the fact that they became very complex with millions of parameters. That is, requiring more resources and time, and being unsuitable for small restricted devices. To contribute in this direction, this paper presents (1) some state-of-the-art lightweight architectures that were specifically designed for small-sized devices, and (2) some recent solutions that have been proposed to optimize/compress classical deep neural networks to allow their dep…
Full Reference Mesh Visual Quality Assessment Using Pre-Trained Deep Network and Quality Indices
2019
In this paper, we propose an objective quality metric to evaluate the perceived visual quality of 3D meshes. Our method relies on pre-trained convolutional neural network i.e VGG to extract features from the distorted mesh and its reference. Quality indices from well-known mesh visual quality metrics are concatenated with the extracted features resulting a global feature vector. this latter is used to learn the support vector regression (SVR) to predict the final quality score. Experimental results from two subjective databases (LIRIS masking database and LIRIS/EPFL general-purpose database) and comparisons with seven objective metrics cited in the state-of-the-art demonstrate the effective…
Automatic Detection of Infantile Hemangioma using Convolutional Neural Network Approach
2020
Infantile hemangioma is the most common tumor of childhood. This study proposes an automatic detection as a preliminary step for a further accurate monitoring tool to evaluate the clinical status of hemangioma. For the detection of hemangioma pixels, a convolutional neural network (CNN) was trained on patches of two classes (hemangioma and nonhemangioma) from the train dataset, and then it was used to classify all the pixels of the region of interest from the test dataset. In order to evaluate the results of segmentation obtained with CNN, the region of interest of the test dataset was also segmented using two classical methods of segmentation: fuzzy c-means clustering (FCM) and segmentatio…
A Sentiment Enhanced Deep Collaborative Filtering Recommender System
2021
Recommender systems use advanced analytic and learning techniques to select relevant information from massive data and inform users’ smart decision-making on their daily needs. Numerous works exploiting user’s sentiments on products to enhance recommendations have been introduced. However, there has been relatively less work exploring higher-order user-item features interactions for sentiment enhanced recommender system. In this paper, a novel Sentiment Enhanced Deep Collaborative Filtering Recommender System (SE-DCF) is developed. The architecture is based on a Neural Attention network component aggregated with the output predictions of a Convolution Neural Network (CNN) recommender. Speci…
Reduced Reference Mesh Visual Quality Assessment Based on Convolutional Neural Network
2018
3D meshes are usually affected by various visual distortions during their transmission and geometric processing. In this paper we propose a reduced reference method for mesh visual quality assessment. The method compares features extracted from the distorted mesh and the original one using a convolutional neural network in order to estimate the visual quality score. The perceptual distance between two meshes is computed as the Kullback-Leibler divergence between the two sets of feature vectors. Experimental results from two subjective databases (LIRIS masking database and LIRIS/EPFL general purpose database) and comparisons with seven objective metrics cited in the state-of-the-art demonstr…
Deep learning model deploying on embedded skin cancer diagnostic device
2020
The number of research papers, where neural networks are applied in medical image analysis is growing. There is a proof that Convolutional Neural Networks (CNN) are able to differentiate skin cancer from nevi with greater accuracy than experienced specialists on average (sensitivity 82% and 73% accordingly).1 Team's latest research2 allows achieving even greater accuracy, by using specific narrow-band illumination. Nevertheless, the overall probability of early skin cancer detection depends on the availability of diagnostic tools. If screening tools will be available to a high number of general practices, the chance of disease detection will increase. The previous research3 shows that scala…