Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Key advancements are highlighted, including the integration of Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for image-based detection and anomaly identification. …”
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A bi-stream transformer for single-image dehazing by Mingrui Wang, Jinqiang Yan, Chaoying Wan, Guowei Yang, Teng Yu

“…Deep-learning methods, such as encoder-decoder networks, have achieved impressive results in image dehazing. …”
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Malware detection based on visualization of recombined API instruction sequence by Hongyu Yang, Yupei Zhang, Liang Zhang, Xiang Cheng

“…The feature image is then fed into the self-built lightweight malware feature image convolution neural network. The experimental results indicate that the detection accuracy of this method is 98.66% and that it has high performance indicators and detection speed for malware detection.…”
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Boosting Cervical Cancer Prediction Leveraging a Hybrid FT-Transformer Model by Md Emamul Hossen, S. M. Mahim, Shakib Al Hasan, Md Khairul Islam, Md Shohidul Islam, Salahuddin Khan, Mohammad Alibakhshikenari, Peiman Parand, Md Sipon Miah

“…To address this critical clinical need, we propose an innovative Hybrid FT-Transformer model that synergistically integrates a Feature Tokenization (FT) Transformer with Depthwise convolutional neural networks and Long Short-Term Memory (LSTM) networks for precise CC prediction. …”
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Remaining Useful Life Estimation of Used Li-Ion Cells With Deep Learning Algorithms Without First Life Information by I. Sanz-Gorrachategui, Y. Wang, A. Guillen-Asensio, A. Bono-Nuez, B. Martin-del-Brio, P. V. Orlik, P. Pastor-Flores

“…We compute features such as incremental capacity curves, and other health indicators from the measured voltage and current waveforms of the used cell. These features are automatically processed by deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors by Zhangli Lu, Huiying Zhou, Honghao Lyu, Haiteng Wu, Shaohua Tian, Geng Yang

“…Thus, to address the limitations of manual scoring and complexities of capturing gait features, we proposed an automated BBS assessment system using an attention-based deep learning algorithm with IMU data, integrating convolutional neural networks (CNNs) for spatial feature extraction, bidirectional long short-term memory (Bi-LSTM) networks for temporal modeling, and attention mechanisms to emphasize informative features. …”
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On the usage of artificial intelligence in leprosy care: A systematic literature review. by Hilson Gomes Vilar de Andrade, Elisson da Silva Rocha, Kayo H de Carvalho Monteiro, Cleber Matos de Morais, Danielle Christine Moura Dos Santos, Dimas Cassimiro Nascimento, Raphael A Dourado, Theo Lynn, Patricia Takako Endo

“…We have excluded works due duplication, couldn't be retrieved and quality assessment. Results show that current research is focused primarily on the identification of symptoms using image based classification using three main techniques, neural networks, convolutional neural networks, and support vector machines; a small number of studies focus on other thematic areas of leprosy care. …”
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Advancing Rice Grain Impurity Segmentation with an Enhanced SegFormer and Multi-Scale Feature Integration by Xiulin Qiu, Hongzhi Yao, Qinghua Liu, Hongrui Liu, Haozhi Zhang, Mengdi Zhao

“…During the rice harvesting process, severe occlusion and adhesion exist among multiple targets, such as rice, straw, and leaves, making it difficult to accurately distinguish between rice grains and impurities. To address the current challenges, a lightweight semantic segmentation algorithm for impurities based on an improved SegFormer network is proposed. …”
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Deep learning-based crop health enhancement through early disease prediction by Venkata Santhosh Yakkala, Krishna Vamsi Nusimala, Badisa Gayathri, Sriya Kanamarlapudi, S. S. Aravinth, Ayodeji Olalekan Salau, S. Srithar

“…Leveraging the power of machine learning algorithms, particularly Convolutional Neural Networks (CNNs) and ResNet-9 architecture, this research seeks to transform the process of detecting plant diseases. …”
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Deep learning (DL)‐based advancements in prostate cancer imaging: Artificial intelligence (AI)‐based segmentation of 68Ga‐PSMSA PET for tumor volume assessment by Sharjeel Usmani, Khulood Al Riyami, Subash Kheruka, Shah P Numani, Rashid al Sukaiti, Maria Ahmed, Nadeem Pervez

“…This review discusses the principles underlying AI‐based segmentation algorithms, including convolutional neural networks, and their applications in PC imaging. …”
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Implementing a deep learning model for defect classification in Thai Arabica green coffee beans by Sujitra Arwatchananukul, Dan Xu, Phasit Charoenkwan, Sai Aung Moon, Rattapon Saengrayap

“…This research developed a classification model based on a Convolutional Neural Network to detect 17 types of defects in green coffee beans. …”
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Sticky Trap-Embedded Machine Vision for Tea Pest Monitoring: A Cross-Domain Transfer Learning Framework Addressing Few-Shot Small Target Detection by Kunhong Li, Yi Li, Xuan Wen, Jingsha Shi, Linsi Yang, Yuyang Xiao, Xiaosong Lu, Jiong Mu

“…Additionally, the original C2f module is replaced with lighter convolutional modules to reduce the loss of information about small target pests. …”
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Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification by Iftekhar Ahmed, Biggo Bushon Routh, Md. Saidur Rahman Kohinoor, Shadman Sakib, Md Mahfuzur Rahman, Farag Azzedin

“…Skin cancer, with its rising global prevalence, remains a crucial healthcare challenge, necessitating efficient and early detection for better patient outcomes. While deep convolutional neural networks have advanced image classification, current models struggle with diverse lesion types, variable image quality, and dataset imbalances. …”
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TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction by Wenlong Wang, Peng Yu, Mengmeng Li, Xiaojing Zhong, Yuanrong He, Hua Su, Yunxuan Zhou

“…Therefore, methods integrating convolutional neural networks (CNNs) and visual transformers (ViTs) are popular nowadays. …”
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Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones by Andrew P. Creagh, Frank Dondelinger, Florian Lipsmeier, Michael Lindemann, Maarten De Vos

“…<italic>Methods:</italic> Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant&#x0027;s daily ambulatory-related disease severity, longitudinally over a 24-week study. …”
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3D-SCUMamba: An Abdominal Tumor Segmentation Model by Juwita, Ghulam Mubashar Hassan, Amitava Datta

“…Existing deep learning models typically adopt encoder-decoder architectures integrating convolutional layers with global dependency modeling to capture broader contextual information around tumors. …”
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PRDAGE: a prescription recommendation framework for traditional Chinese medicine based on data augmentation and multi-graph embedding by Zhihua Wen, Yunchun Dong, Lihong Peng, Longxin Zhang, Junfeng Yan

“…Additionally, we developed a multi-layer embedding method for symptoms and herbs, using Sentence Bert (SBert) and graph convolutional networks. The aim of this multi-layer embedding method is to capture and represent the semantic information of symptoms and herbs, as well as the complex relationships between them. …”
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An Improved V-Net Model for Thyroid Nodule Segmentation by Büşra Yetginler, İsmail Atacak

“…This study proposes an improved V-Net segmentation model based on fully convolutional neural networks (V-Net) and squeeze-and-excitation (SE) mechanisms for detecting thyroid nodules in two-dimensional image data. …”
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Deep-learning based morphological segmentation of canine diffuse large B-cell lymphoma by Kenneth Ancheta, Androniki Psifidi, Andrew D. Yale, Sophie Le Calvez, Jonathan Williams

“…This study explores the use of convolutional neural networks (CNNs) to differentiate cDLBCL from non-neoplastic lymph nodes, specifically reactive lymphoid hyperplasia (RLH). …”
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Deep Learning-Based Sound Source Localization: A Review by Kunbo Xu, Zekai Zong, Dongjun Liu, Ran Wang, Liang Yu

“…In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. …”
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