A Deep Learning Framework for the Classification of Brazilian Coins by Debabrata Swain, Viral Rupapara, Amro Nour, Santosh Satapathy, Biswaranjan Acharya, Shakti Mishra, Ali Bostani

“…Our proposed deep learning framework leverages state-of-the-art convolutional neural networks (CNNs) to address these challenges. …”
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Accurate classification of benign and malignant breast tumors in ultrasound imaging with an enhanced deep learning model by Baoqin Liu, Shouyao Liu, Zijian Cao, Junning Zhang, Xiaoqi Pu, Junjie Yu

“…BackgroundBreast cancer is the most common malignant tumor in women worldwide, and early detection is crucial to improving patient prognosis. …”
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Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds by Muhammad Hamza Tariq, Haseeb Sultan, Rehan Akram, Seung Gu Kim, Jung Soo Kim, Muhammad Usman, Hafiz Ali Hamza Gondal, Juwon Seo, Yong Ho Lee, Kang Ryoung Park

“…To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. …”
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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

“…Network pruning facilitates the deployment of convolutional neural networks in resource-limited environments by reducing redundant parameters. …”
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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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