Dynamic Gesture Recognition and Interaction of Monocular Camera Based on Deep Learning by SUNBo wen, YU Feng

“…Most of the existing gesture recognition applications are based on specific devices, such as Kinect, Leap Motion, etc. …”
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Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images by Mohammadreza Behnia, Touba Torabipour, Safieh Siadat

“…Lung infection is the most dangerous sign of Covid 19. X-ray images are the most effective means of diagnosing this virus. …”
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Colorectal Cancer Detection Tool Developed with Neural Networks by Alex Ede Danku, Eva Henrietta Dulf, Alexandru George Berciu, Noemi Lorenzovici, Teodora Mocan

“…In the last two decades, there has been a considerable surge in the development of artificial intelligence. Imaging is most frequently employed for the diagnostic evaluation of patients, as it is regarded as one of the most precise methods for identifying the presence of a disease. …”
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Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk Hα Images by GaoFei Zhu, GangHua Lin, Xiao Yang, Cheng Zeng

“…Solar filaments are one of the most prominent features observed on the Sun, and their evolutions are closely related to various solar activities, such as flares and coronal mass ejections. …”
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Hybrid Attention and Multiscale Module for Alzheimer's Disease Classification by WANG Yuanjun

“…Alzheimer's disease is the most common neurodegenerative disorder among dementia, characterized by slow disease progression and complex imaging features. …”
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Artificial neural networks in cardiology: analysis of graphic data by P. S. Onishchenko, K. Yu. Klyshnikov, E. A. Ovcharenko

“…The general principle of work of the technology under consideration was described, the results were shown, and the main areas of application of this technology in the studies under consideration were described. For most of the studies, sample sizes were given. The author’s view on the development of convolutional neural networks in medicine was presented and some limiting factors for their distribution were listed.Conclusion. …”
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Neural network pruning based on channel attention mechanism by Jianqiang Hu, Yang Liu, Keshou Wu

“…However, most of the existing methods ignore the differences in the contributions of the output feature maps. …”
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Capsule network based analysis of histopathological images of oral squamous cell carcinoma by Santisudha Panigrahi, Jayshankar Das, Tripti Swarnkar

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Bacterial Disease Detection of Cherry Plant Using Deep Features by Hatice Kayhan, Emrah Dönmez, Yavuz Ünal

“…The features of the cherry plant disease will be determined by using a pre-trained convolutional neural network (CNN) model which is DarkNet-19, within the scope of this study. …”
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Gaze Estimation Network Based on Multi-Head Attention, Fusion, and Interaction by Changli Li, Fangfang Li, Kao Zhang, Nenglun Chen, Zhigeng Pan

“…Specifically, multi-head attention and channel attention are used to fuse features from both eyes, and a face and eye interaction module is designed to highlight the most important facial features guided by the eye features; in addition, the channel attention in the Convolutional Block Attention Module (CBAM) is replaced with minimum pooling instead of maximum pooling, and a shortcut connection is added to enhance the network’s attention to eye region details. …”
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Inferring Body Measurements from 2D Images: A Comprehensive Review by Hezha Mohammedkhan, Hein Fleuren, Çíçek Güven, Eric Postma

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Explainable CNN for brain tumor detection and classification through XAI based key features identification by Shagufta Iftikhar, Nadeem Anjum, Abdul Basit Siddiqui, Masood Ur Rehman, Naeem Ramzan

“…Our work addresses these limitations by introducing a novel methodology that combines Explainable AI (XAI) techniques with a Convolutional Neural Network (CNN) architecture. The major contribution of this paper is ensuring that the model focuses on the most relevant features for tumor detection and classification, while simultaneously reducing complexity, by minimizing the number of layers. …”
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Potato Plant Variety Identification Study Based on Improved Swin Transformer by Xue Xing, Chengzhong Liu, Junying Han, Quan Feng, Enfang Qi, Yaying Qu, Baixiong Ma

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Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification by Ruimin Han, Shuli Cheng, Shuoshuo Li, Tingjie Liu

“…Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. …”
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Recognition of Strawberry Powdery Mildew in Complex Backgrounds: A Comparative Study of Deep Learning Models by Jingzhi Wang, Jiayuan Li, Fanjia Meng

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ProBoost: Reducing Uncertainty Using a Boosting Method for Probabilistic Models by Fabio Mendonca, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, Antonio G. Ravelo-Garcia, Mario A. T. Figueiredo

“…The learners herein considered are standard convolutional neural networks, and the probabilistic models underlying the uncertainty estimation use either variational inference or Monte Carlo dropout. …”
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3D Anisotropic Asymmetric Pyramid (AAP-Net) Network Model for Brain Metastases Segmentation on Multi-Sequence MRI by Omar Farghaly, Priya Deshpande

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Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces by M.A. Dalhat, Sami A. Osman

“…This study employed convolutional neural networks (CNNs) for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces. …”
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An imaging and genetic-based deep learning network for Alzheimer's disease diagnosis by Yuhan Li, Donghao Niu, Keying Qi, Dong Liang, Dong Liang, Xiaojing Long, Xiaojing Long

“…While deep learning methods based on MRI have demonstrated promising results for early AD diagnosis, the limited dataset size has led most AD studies to lean on statistical approaches within the realm of imaging genetics. …”
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Automating field‐based floral surveys with machine learning by Nicholas Sookhan, Shane Sookhan, Devlin Grewal, J. Scott MacIvor

“…However, the training process, particularly manual data annotation, was the most time‐consuming component of the study. Practical implication: Overall, the analysis provided valuable insights into automated flower classification and abundance estimation using drone imagery and machine learning. …”
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