Search results for "convolutional neural network"
showing 10 items of 179 documents
Classification of Heart Sounds Using Convolutional Neural Network
2020
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…
Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
2020
The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep lea…
Deep Convolutional Neural Networks for Fire Detection in Images
2017
Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balance…
Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
2022
Skin cancer is one of the most prevalent of all cancers. Because of its being widespread and externally observable, there is a potential that machine learning models integrated into artificial intelligence systems will allow self-screening and automatic analysis in the future. Especially, the recent success of various deep machine learning models shows promise that, in the future, patients could self-analyse their external signs of skin cancer by uploading pictures of these signs to an artificial intelligence system, which runs such a deep learning model and returns the classification results. However, both patients and dermatologists, who might use such a system to aid their work, need to …
Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering
2022
Abstract Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segme…
Domain Adaptation of Landsat-8 and Proba-V Data Using Generative Adversarial Networks for Cloud Detection
2019
Training machine learning algorithms for new satellites requires collecting new data. This is a critical drawback for most remote sensing applications and specially for cloud detection. A sensible strategy to mitigate this problem is to exploit available data from a similar sensor, which involves transforming this data to resemble the new sensor data. However, even taking into account the technical characteristics of both sensors to transform the images, statistical differences between data distributions still remain. This results in a poor performance of the methods trained on one sensor and applied to the new one. In this this work, we propose to use the generative adversarial networks (G…
A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.
2020
To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2 ± 2.8 %, and…
Deep Convolutional Neural Network Based Object Detection Inference Acceleration Using FPGA
2022
Object detection is one of the most challenging yet essential computer vision research areas. It means labeling and localizing all known objects of interest on an input image using tightly fit rectangular bounding boxes around the objects. Object detection, having passed through several evolutions and progressions, nowadays relies on the successes of image classification networks based on deep convolutional neural networks. However, as the depth and complication of convolutional neural networks increased, detection speed reduced, and accuracy increased. Unfortunately, most computer vision applications, such as real-time object tracking on an embedded system, requires lightweight, fast and a…
Detection of Hate Speech Spreaders using Convolutional Neural Networks
2021
In this paper we describe a deep learning model based on a Convolutional Neural Network (CNN). The model was developed for the Profiling Hate Speech Spreaders (HSSs) task proposed by PAN 2021 organizers and hosted at the 2021 CLEF Conference. Our approach to the task of classifying an author as HSS or not (nHSS) takes advantage of a CNN based on a single convolutional layer. In this binary classification task, on the tests performed using a 5-fold cross validation, the proposed model reaches a maximum accuracy of 0.80 on the multilingual (i.e., English and Spanish) training set, and a minimum loss value of 0.51 on the same set. As announced by the task organizers, the trained model presente…
Identifying Images with Ladders Using Deep CNN Transfer Learning
2019
Deep Convolutional Neural Networks (CNNs) as well as transfer learning using their pre-trained models often find applications in image classification tasks. In this paper, we explore the utilization of pre-trained CNNs for identifying images containing ladders. We target a particular use case, where an insurance firm, in order to decide the price for workers’ compensation insurance for its client companies, would like to assess the risk involved in their workplace environments. For this, the workplace images provided by the client companies can be utilized and the presence of ladders in such images can be considered as a workplace hazard and therefore an indicator of risk. To this end, we e…