20 papers · ranked by Valyu relevance
Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan + 4 more
'Sengul Dogan' 'Mehmet Baygin' 'Turker Tuncer' 'U. R. Acharya' 'Stefanos Kollias'] This work aims to develop a novel convolutional neural network (CNN) named ResNet50 to detect various gastrointestinal diseases using a new ResNet50-based deep feature engineering model with endoscopy images. The novelty of this work is…
Sutthirak Tangruangkiat, Napatsorn Chaiwongkot, Chayanon Pamarapa, Thanatcha Rawangwong + 7 more
2.7### CNN-ResNet50 architecture This study adopted and modified a preexisting pretrained convolutional neural network (CNN) for image recognition known as Residual Network 50 (ResNet50) to develop a diagnostic model for FLLs. Our modified model is based on the original ResNet50 architecture introduced by He et al. in…
Manuel D. Morales, Javier M. Antelis, Claudia Moreno
core-collapse supernova gravitational waves Authors: ['Manuel D. Morales' 'Javier M. Antelis' 'Claudia Moreno'] We present the results of a detailed study on the detectability of the High Frequency Feature (HFF) in core-collapse supernova (CCSN) gravitational wave (GW) signals. We applied Residual Neural Networks…
Jin Liang, Wenping Jiang
Tomato leaf disease identification is difficult owing to the variety of diseases and complex causes, for which the method based on the convolutional neural network is effective. While it is challenging to capture key features or tends to lose a large number of features when extracting image features by applying this…
Essam H. Houssein, Marwa M. Emam, Abdelmgeid A. Ali
Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection…
Jun Hyong Ahn, Heung Cheol Kim, Jong Kook Rhim, Jeong Jin Park + 5 more
'Dick Sigmund' 'Min Chan Park' 'Jae Hoon Jeong' 'Jin Pyeong Jeon' 'Mayra Paolillo'] Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for…
Sanya Sinha, Nilay Gupta
According to the World Malaria Report of 2022, 247 million cases of malaria and 619,000 related deaths were reported in 2021. This highlights the predominance of the disease, especially in the tropical and sub-tropical regions of Africa, parts of South-east Asia, Central and Southern America. Malaria is caused due to…
Sofia A. Bengoa Luoni, Riccardo Ricci, Melanie A. Corzo, Genc Hoxha + 2 more
Leaf senescence is a complex mechanism governed by multiple genetic and environmental variables that affect crop yield. It is the last stage of leaf development and is characterized by an active decline in the photosynthetic rate, nutrient recycling, and cell death. Leaf senescence begins in the lower leaves, and…
Tal Ridnik, Hussam Lawen, Asaf Noy, Emanuel Ben + 2 more
'Baruch Gilad Sharir' 'Itamar Friedman'] Many deep learning models, developed in recent years, reach higher ImageNet accuracy than ResNet50, with fewer or comparable FLOPs count. While FLOPs are often seen as a proxy for network efficiency, when measuring actual GPU training and inference throughput, vanilla ResNet50…
Alexander Kensert, Philip J Harrison, Ola Spjuth
Quantification and identification of cellular phenotypes from high content microscopy images have proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell morphology, which…
Marco Parola, Alice Nannini, Stefano Poleggi
To implement a good Content Based Image Retrieval (CBIR) system, it is essential to adopt efficient search methods. One way to achieve this results is by exploiting approximate search techniques. In fact, when we deal with very large collections of data, using an exact search method makes the system very slow. In this…
Ian Groves, Jacob Holmshaw, David Furley, Matthew Towers + 3 more
Deep learning is a powerful tool for image classification, yet training deep learning models often requires large datasets, which are not always available in biological research. Here we use a small dataset to train an accurate classifier by employing data augmentation regimes and Bayesian optimisation. We establish a…
Chengyin Hu, Weiwen Shi, Chao Li, Jialiang Sun + 3 more
'Junqi Wu' 'Guijian Tang'] Abstract. Deep neural networks (DNNs) have made remarkable strides in various computer vision tasks, including image classification, segmentation, and object detection. However, recent research has revealed a vulnerability in advanced DNNs when faced with deliberate manipulations of input…
John-William Sidhom, Alexander S. Baras
Deep learning is an area of artificial intelligence that has received much attention in the past few years due to both an increase in computational power with the increased use of graphics processing units (GPU’s) for computational analyses and the performance of these class of algorithms on visual recognition tasks.…
E. Haffner‐Staton, L. Avanzini, A. La Rocca, S. A. Pfau + 1 more
A pre-trained convolution neural network based on residual error functions (ResNet) was applied to the classification of soot and non-soot carbon nanoparticles in TEM images. Two depths of ResNet, one 18 layers deep and the other 50 layers deep, were trained using training-validation sets of increasing size (containing…
Loris Nanni, Michelangelo Paci, Sheryl Brahnam, Alessandra Lumini + 2 more
Convolutional neural networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by…
Jonathan Zhang, Bowen Xie, Xin Wu, Rahul Ram + 1 more
| 1 | Introduction | | 3 | | --- | --- | --- | --- | | 2 | Methodology | | 4 | | | 2.1 | Overview of Proposed Study | 4 | | | 2.2 Preprocessing Stage | | 4 | | | 2.3 Network Architecture | | 5 | | | 2.4 | Model Compilation and Training Process | 7 | | 3 | Results | | 7 | | 4 | Discussion | | 10 | | | 4.1 | Result…
A. Doerig, A. Bornet, O. H. Choung, M. H. Herzog
Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have suggested that current ffCNNs do not make use of global shape information.…
Benjamin Hoar, Weitong Zhang, Shuangning Xu, Rana Deeba + 3 more
For decades, employing cyclic voltammetry for mechanistic investigation demands manual inspection of voltammograms. Here we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a electrochemical probable mechanism among five of the most common ones in homogenous…
Emanuele Quattrocchi, Baptiste Py, Adeleke Maradesa, Quentin Meyer + 2 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique widely used to evaluate the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as a model-free alternative to equivalent circuits and physical models to circumvent the inherent challenges of EIS…