Search
Search Results
-
Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs
Diabetic retinopathy (DR) is a common eye disorder that can lead to vision problems and blindness, necessitating accurate grading for effective...
-
Use of artificial neural networks in architecture: determining the architectural style of a building with a convolutional neural networks
The discussion of "can machines think?" which started with the invention of the modern computer, brought along the question of "can machines design?"...
-
Convolutional Neural Networks and Architectures
This chapter briefly introduces Convolutional Neural Networks (CNNs). One of the first CNNs is proposed in [41] (known as LeNet) to deal with...
-
Symmetry-structured convolutional neural networks
We consider convolutional neural networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions. Such networks arise in...
-
Convolutional Neural Networks
Artificial neural networks have flourished in recent years in the processing of unstructured data, especially images, text, audio, and speech....
-
Residential building type classification from street-view imagery with convolutional neural networks
Computer vision techniques are increasingly used to develop efficient and automatic methods that provide alternative data sources. Micro-level...
-
Convolutional Neural Networks
Convolutional neural networks (CNNs) are a category of neural networks that can be used to identify spatial patterns in a robust manner. They achieve...
-
Exploring adversarial examples and adversarial robustness of convolutional neural networks by mutual information
Convolutional neural networks (CNNs) are susceptible to adversarial examples, which are similar to original examples but contain malicious...
-
Recursive least squares method for training and pruning convolutional neural networks
Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage...
-
Convolutional Neural Networks for Medical Applications
Convolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge,...
-
Diagnosis of COVID-19 CT Scans Using Convolutional Neural Networks
Machine learning technology, particularly neural networks, provides useful tools for diagnosing diseases. This study focuses on how convolutional...
-
Gaze estimation using convolutional neural networks
Numerous investigations on gaze estimate techniques for analyzing human behavior have been made in recent years, the majority of which have focused...
-
Ctnet: rethinking convolutional neural networks and vision transformer for medical image segmentation
Convolutional architectures have demonstrated remarkable success in various vision tasks, offering efficient learning through their inherent...
-
Convolutional Neural Networks
A series of successful applications of Convolutional Neural Networks (CNNs) in various computer vision competitions in 2011 and 2012 were a major...
-
Improving image classification of one-dimensional convolutional neural networks using Hilbert space-filling curves
Convolutional neural networks (CNNs) have significantly contributed to recent advances in machine learning and computer vision. Although initially...
-
A Multi-objective Optimization Model for Redundancy Reduction in Convolutional Neural Networks
Until now, convolutional neural networks (CNNs) still among the powerful and robust deep neural networks that proved its efficiency through several...
-
Lung and colon cancer detection with convolutional neural networks and adaptive histogram equalization
Lung and colon cancers are responsible for a considerable number of deaths annually, with lung cancer being the most prevalent and colon cancer...
-
Convolutional neural networks for pattern classifying based on parameterized predefined sequence of image filters
Convolutional neural networks (CNNs) are used to solve pattern classification problems. As this algorithm is based on establishing a relationship...
-
Improved generalization performance of convolutional neural networks with LossDA
In recent years, convolutional neural networks (CNNs) have been used in many fields. Nowadays, CNNs have a high learning capability, and this...
-
A dimensionality reduction approach for convolutional neural networks
The focus of this work is on the application of classical Model Order Reduction techniques, such as Active Subspaces and Proper Orthogonal...
