Deep Learning Evaluation of Glaucoma Detection Using Fundus Photographs in Highly Myopic Populations by Yen-Ying Chiang, Ching-Long Chen, Yi-Hao Chen

“…We used two classification models with the convolutional block attention module (CBAM), an attention mechanism module that enhances the performance of convolutional neural networks (CNNs), to investigate glaucoma cases. …”
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AI-ming backwards: Vanishing archaeological landscapes in Mesopotamia and automatic detection of sites on CORONA imagery. by Alessandro Pistola, Valentina Orrù, Nicolò Marchetti, Marco Roccetti

“…The initial Bing-based convolutional network model was re-trained using CORONA satellite imagery for the district of Abu Ghraib, west of Baghdad, central Mesopotamian floodplain. …”
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A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance by Noushin Saba, Afia Zafar, Mohsin Suleman, Kainat Zafar, Shahneer Zafar, Adil Ali Saleem, Hafeez Ur Rehman Siddiqui, Muhammad Iqbal, Syed Sajid Ullah

“…We employed EfficientNet, a state-of-the-art convolutional neural network, to extract intricate features from histopathological images, alongside the Non-dominated Sorting Genetic Algorithm II for optimal feature selection. …”
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Beef Traceability Between China and Argentina Based on Various Machine Learning Models by Xiaomeng Xiang, Chaomin Zhao, Runhe Zhang, Jing Zeng, Liangzi Wang, Shuran Zhang, Diego Cristos, Bing Liu, Siyan Xu, Xionghai Yi

“…The classification accuracy of the PLS-DA model built on these results was 98.8%, while the prediction accuracy was 94.12% for the convolutional neural network (CNN) and 82.35% for the Random Forest algorithm. …”
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A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning by Cheng Wang, Zhen Yu, Zhou Long, Hui Zhao, Zhenwei Wang

“…Therefore, early diagnosis, classification of severity and related treatment are crucial for the patients. Current DFU classification methods often require experienced doctors to manually classify the severity, which is time-consuming and low accuracy. …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

“…To address these challenges, we propose Med-DGTN, a dynamically integrated framework designed to advance multi-label classification performance in clinical imaging analytics.MethodsThe proposed Med-DGTN (Dynamic Graph Transformer Network with Adaptive Wavelet Fusion) introduces three key innovations: (1) A cross-modal alignment mechanism integrating convolutional visual patterns with graph-based semantic dependencies through conditionally reweighted adjacency matrices; (2) Wavelet-transform-enhanced dense blocks (WTDense) employing multi-frequency decomposition to amplify low-frequency pathological biomarkers; (3) An adaptive fusion architecture optimizing multi-scale feature hierarchies across spatial and spectral domains.ResultsValidated on two public medical imaging benchmarks, Med-DGTN demonstrates superior performance across modalities: (1) Achieving a mean average precision (mAP) of 70.65% on the retinal imaging dataset (MuReD2022), surpassing previous state-of-the-art methods by 2.68 percentage points. (2) On the chest X-ray dataset (ChestXray14), Med-DGTN achieves an average Area Under the Curve (AUC) of 0.841. …”
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The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study by Pranav Parekh, Richard Oyeleke, Tejas Vishwanath

“…MethodsWe started by performing classification using a convolutional neural network (CNN), followed by using explainable artificial intelligence to localize the image features responsible for the CNN output. …”
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Multi-Objective Scheduling for Green Flexible Assembly Job-Shop System via Multi-Agent Deep Reinforcement Learning With Game Theory by Xiao Wang, Zhongyuan Liang, Peisi Zhong, Dongmin Li, Hongqi Li, Mei Liu

“…A mathematical model is formulated to describe the FAJS problem, which then is translated into a Markov Decision Process (MDP) where an agent directly selects behavioral policies according to the processing state of the current decision point. The processing state feature data that uses a deep convolutional neural network to fit the value function is extracted from three matrices including the processing time matrix, task assignment Boolean matrix, and an adjacency matrix. …”
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Enhanced Online Continuous Brain-Control by Deep Learning-Based EEG Decoding by Jiaheng Wang, Lin Yao, Yueming Wang

“…A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding. …”
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Nystromformer based cross-modality transformer for visible-infrared person re-identification by Ranjit Kumar Mishra, Arijit Mondal, Jimson Mathew

“…Our framework begins by extracting hierarchical features from both RGB and IR images through a shared convolutional neural network (CNN) backbone, ensuring the preservation of modality-specific characteristics. …”
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Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review by Mahdieh Poodineh Moghadam, Zabih Allah Moghadam, Mohammad Reza Chalak Qazani, Pawel Plawiak, Roohallah Alizadehsani

“…Our findings reveal that Random Forest, logistic regression, and convolutional neural network (CNN) are the most frequently used AI techniques, typically evaluated by accuracy metrics and the area under the curve (AUC). …”
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High-Fidelity Depth Map Reconstruction System With RGB-Guided Super Resolution CNN and Cross-Calibrated Chaos LiDAR by Yu-Chun Ding, Chia-Yu Chang, Pei-Rong Li, Chao-Tsung Huang, Yung-Chen Lin, Tsung Chen, Wei-Lun Lin, Cheng-Ting Lee, Fan-Yi Lin, Yuan-Hao Huang

“…In this work, we propose a depth map reconstruction system that integrates an RGB-guided depth map super-resolution convolutional neural network (CNN) into a stand-alone Chaos LiDAR depth sensor. …”
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A Literature Review on Artificial Intelligence in Dermatological Diagnosis and Tissue Microscopy by Paul-Vasile Vezeteu, Andrei-Daniel Andronescu, Dumitru-Iulian Nastac

“…Key deep learning methodologies, such as convolutional neural networks (CNNs), transfer learning, and explainable AI, are examined in the context of current medical practices. …”
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An Investigation on Prediction of Infrastructure Asset Defect with CNN and ViT Algorithms by Nam Lethanh, Tu Anh Trinh, Mir Tahmid Hossain

“…Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. …”
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Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review by Khaldoon Alhusari, Salam Dhou

“…The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. …”
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Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data by Yilin Bao, Xiangtian Meng, Weimin Ruan, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen

“…Next, a hybrid deep learning model, Multimodal Transformer Mechanism-Convolutional Neural Network-Convolutional Long Short-Term Memory (MT-CNN-ConvLSTM, MCCL), is constructed to enhance predictive accuracy and generalizability. …”
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A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications by Ibomoiye Domor Mienye, Theo G. Swart

“…Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). …”
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Temporal pyramid attention‐based spatiotemporal fusion model for Parkinson's disease diagnosis from gait data by Xiaomin Pei, Huijie Fan, Yandong Tang

“…First, by using the temporal pyramid attention module, multiscale temporal attention is obtained from raw sequences. Second, 1D convolutional neural network and bidirectional long short‐term memory layers are used together to learn spatial fusion features from multiple channels in the spatial domain to obtain multichannel, multiscale fusion features. …”
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A hybrid approach for investigation of electrical characteristics for rod to point electrode in gas insulators by R. Bharanidharan, V. J. Vijayalakshmi, R. V. Maheswari

“…The proposed approach combines the Mother Optimization Algorithm (MOA) and Circular Dilated Convolutional Neural Network (CDCNN), Hence it is named as MOA-CDCNN technique. …”
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Aeroengine Remaining Life Prediction Using Feature Selection and Improved SE Blocks by Hairui Wang, Shijie Xu, Guifu Zhu, Ya Li

“…Considering that the RUL of equipment changes in a progressively more complex manner as the equipment is used over time, we propose an improved squeeze and excitation block (SSE) and combine it with a convolutional neural network (CNN). By enhancing the feature selection ability of CNN through segmented squeeze and excitation block, the model can focus on important information within features to effectively improve prediction performance. …”
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