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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Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines by Maria Luigia Natalia De Bonis, Giuseppe Fasano, Angela Lombardi, Carmelo Ardito, Antonio Ferrara, Eugenio Di Sciascio, Tommaso Di Noia

“…Our results show comparable performance between the two pipelines in Leave-One-Site-Out (LOSO) validation, achieving state-of-the-art performance on the independent test set ( $$MAE=3.21$$ M A E = 3.21 with DNN and morphometric features and $$MAE=3.08$$ M A E = 3.08 with a DenseNet-121 architecture). SHAP provided the most consistent and interpretable results, while DeepSHAP exhibited greater variability. …”
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Deep Learning-Based Fully Automatic Segmentation of the Paranasal Sinuses in Chronic Rhinosinusitis Patients Using Computed Tomographic Images by Yuhang Wang, Xiaolei Zhang, Weidong Du, Na Dai, Yi Lyv, Keying Wu, Yiyang Tian, Yuxin Jie, Yu Lin, Weipiao Kang

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Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations by Kota Nakajima, Kazuki Saito, Yasuhiro Tsujimoto, Toshiyuki Takai, Atsushi Mochizuki, Tomoaki Yamaguchi, Ali Ibrahim, Salifou Goube Mairoua, Bruce Haja Andrianary, Keisuke Katsura, Yu Tanaka

“…This study aims to assess the robustness of a convolutional neural network (CNN) model for rice AGB estimation across five locations in three countries, and to demonstrate the feasibility of robust model via a practical approach. …”
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MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition by Khan Abrar Shams, Md. Rafid Reaz, Mohammad Ryan Ur Rafi, Sanjida Islam, Md. Shahriar Rahman, Rafeed Rahman, Md. Tanzim Reza, Mohammad Zavid Parvez, Subrata Chakraborty, Biswajeet Pradhan, Abdullah Alamri

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