Accuracy of Deep Learning Algorithms for the Diagnosis of Retinopathy of Prematurity by Fundus Images: A Systematic Review and Meta-Analysis by Jingjing Zhang, Yangyang Liu, Toshiharu Mitsuhashi, Toshihiko Matsuo

“…Retinopathy of prematurity (ROP) occurs in preterm infants and may contribute to blindness. Deep learning (DL) models have been used for ophthalmologic diagnoses. …”
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ThyroNet-X4 genesis: an advanced deep learning model for auxiliary diagnosis of thyroid nodules’ malignancy by Xiaoxue Wang, Yupeng Niu, Hongli Liu, Fa Tian, Qiang Zhang, Yimeng Wang, Yeju Wang, Yijia Li

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A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier by Muhammad Tayyab, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi, Ahmad Jalal, Hui Liu

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Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learning by Jiajian Yu, Yi Gong, Yuan Gao, Hao Sui, Xiaoli Xu, Yanming Liu

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Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients by Benoit Schmauch, Vincent Cabeli, Omar Darwiche Domingues, Jean-Eudes Le Douget, Alexandra Hardy, Reda Belbahri, Charles Maussion, Alberto Romagnoni, Markus Eckstein, Florian Fuchs, Aurélie Swalduz, Sylvie Lantuejoul, Hugo Crochet, François Ghiringhelli, Valentin Derangere, Caroline Truntzer, Harvey Pass, Andre L. Moreira, Luis Chiriboga, Yuanning Zheng, Michael Ozawa, Brooke E. Howitt, Olivier Gevaert, Nicolas Girard, Elton Rexhepaj, Iris Valtingojer, Laurent Debussche, Emanuele de Rinaldis, Frank Nestle, Emmanuel Spanakis, Valeria R. Fantin, Eric Y. Durand, Marion Classe, Katharina Von Loga, Elodie Pronier, Matteo Cesaroni

“…Although recent studies have derived RNA-seq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. …”
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Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning by Guangyan Yang

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Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU by Fath U Min Ullah, Amin Ullah, Noman Khan, Mi Young Lee, Seungmin Rho, Sung Wook Baik

“…Stand by this, we propose an intelligent deep learning-based PLF method where at first the data collected from the house through meters are fed into the pre-assessment step. …”
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Author Correction: AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples by Hyeonseong Jeon, Junhak Ahn, Byunggook Na, Soona Hong, Lee Sael, Sun Kim, Sungroh Yoon, Daehyun Baek

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Classification of Buried Objects From Ground Penetrating Radar Images by Using Second-Order Deep Learning Models by Douba Jafuno, Ammar Mian, Guillaume Ginolhac, Nickolas Stelzenmuller

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An approach for accurate identification and monitoring of species in mangrove forests based on multi-source spectral data and deep learning by Monterrubio-Martínez Erandi, Trujillo-Acatitla Rubicel, Tuxpan-Vargas José, Moreno-Casasola Patricia

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Author Correction: Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning by Zhaoxiang Cai, Sofia Apolinário, Ana R. Baião, Clare Pacini, Miguel D. Sousa, Susana Vinga, Roger R. Reddel, Phillip J. Robinson, Mathew J. Garnett, Qing Zhong, Emanuel Gonçalves

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Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model by Meilia Safitri, Teguh Bharata Adji, Adha Imam Cahyadi

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FE-YOLO: An Efficient Deep Learning Model Based on Feature-Enhanced YOLOv7 for Microalgae Identification and Detection by Gege Ding, Yuhang Shi, Zhenquan Liu, Yanjuan Wang, Zhixuan Yao, Dan Zhou, Xuexiu Zhu, Yiqin Li

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Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

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Deep Learning for Identification and Characterization of Ca ii Absorption Lines: A Multitask Convolutional Neural Network Approach by Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin

“…However, detecting these lines, particularly Ca ii absorption lines, is a time-consuming and laborious process. Existing deep learning methods are prone to false positives and still require extensive manual verification and parameter measurement. …”
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Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO by Umesh Kumar Lilhore, Surjeet Dalal, Magdalena Radulescu, Marinela Barbulescu

“…This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. …”
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Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System by Vaishali Sharma, Prakhar Keshari, Sanjeev Sharma, Kuntal Deka, Ondrej Krejcar, Vimal Bhatia

“…This method introduces an IRS signal-matched (IRSSM) measurement matrix with beam squint for capturing the transmitted signal at a sub-Nyquist rate, taking advantage of the sparsity in the signal and THz channels, and signal recovery using the deep learning (DL) model. Simulation results for symbol error rate (SER) and normalized mean square error (NMSE) performance indicate that the proposed DL-based receiver outperforms conventional recovery algorithms based on orthogonal matching pursuit (OMP) CS-recovery and dictionary-shrinkage estimation (DSE).…”
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A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images by Chaoyue Chen, Yanjie Zhao, Linrui Cai, Haoze Jiang, Yuen Teng, Yang Zhang, Shuangyi Zhang, Junkai Zheng, Fumin Zhao, Zhouyang Huang, Xiaolong Xu, Xin Zan, Jianfeng Xu, Lei Zhang, Jianguo Xu

“…Abstract This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. …”
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Comparing Deep Learning models for mapping rice cultivation area in Bhutan using high-resolution satellite imagery by Biplov Bhandari, Timothy Mayer

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Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments by Ali Saeed, Muazzam A. Khan, Usman Akram, Waeal J. Obidallah, Soyiba Jawed, Awais Ahmad

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