The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review by Jingguo Qu, Xinyang Han, Man-Lik Chui, Yao Pu, Simon Takadiyi Gunda, Ziman Chen, Jing Qin, Ann Dorothy King, Winnie Chiu-Wing Chu, Jing Cai, Michael Tin-Cheung Ying

“…Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. …”
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A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island by Pavlos Fylaktos, George P. Petropoulos, Ioannis Lemesios

“…The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. …”
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End-Edge Collaborative Lightweight Secure Federated Learning for Anomaly Detection of Wireless Industrial Control Systems by Chi Xu, Xinyi Du, Lin Li, Xinchun Li, Haibin Yu

“…Specifically, we first design a residual multihead self-attention convolutional neural network for local feature learning, where the variability and dependence of spatial-temporal features can be sufficiently evaluated. …”
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AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model by Mojtaba Sirati-Amsheh, Elham Shabaninia, Ali Chaparian

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CVT-HNet: a fusion model for recognizing perianal fistulizing Crohn’s disease based on CNN and ViT by Lanlan Li, Ziyue Wang, Chongyang Wang, Tao Chen, Ke Deng, Hong’an Wei, Dabiao Wang, Juan Li, Heng Zhang

“…This makes it suitable for real-world applications where variability in data is common.These findings emphasize its effectiveness in clinical contexts.…”
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Using deep learning for thyroid nodule risk stratification from ultrasound images by Yasaman Sharifi, Morteza Danay Ashgzari, Susan Shafiei, Seyed Rasoul Zakavi, Saeid Eslami

“…Background: Interpreting thyroid ultrasound images is a tedious task and is prone to interobserver variability. This study proposes a computer-aided diagnosis system (CAD) for thyroid nodule risk classification and management recommendations based on the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS), which uses a deep learning framework to increase diagnostic accuracy and reliability. …”
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An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification by M. Sundara Srivathsan, S. Alden Jenish, K. Arvindhan, R. Karthik

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Maize yield estimation in Northeast China’s black soil region using a deep learning model with attention mechanism and remote sensing by Xingke Li, Yunfeng Lyu, Bingxue Zhu, Lushi Liu, Kaishan Song

“…The relative importance analysis of input variables revealed that Enhanced Vegetation Index (EVI), Sun-Induced Chlorophyll Fluorescence (SIF), and DCM were the most influential factors in yield prediction. …”
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Dynahead-YOLO-Otsu: an efficient DCNN-based landslide semantic segmentation method using remote sensing images by Zheng Han, Bangjie Fu, Zhenxiong Fang, Yange Li, Jiaying Li, Nan Jiang, Guangqi Chen

“…Recent advancements in deep convolutional neural networks (DCNNs) have significantly improved landslides identification using remote sensing images. …”
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Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process by Orsolya Péterfi, Nikolett Kállai-Szabó, Kincső Renáta Demeter, Ádám Tibor Barna, István Antal, Edina Szabó, Emese Sipos, Zsombor Kristóf Nagy, Dorián László Galata

“…After training the model, the performance of the developed system was assessed by analysing the particle size distribution of pellet cores with variable sizes within the 250–850 μm size range. The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores. …”
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Graph Neural Network Classification in EEG-Based Biometric Identification: Evaluation of Functional Connectivity Methods Using Time-Frequency Metric by Roghaieh Ashenaei, Ali Asghar Beheshti Shirazi

“…Despite reduced setup complexity, our GCNN achieves over 98% identification accuracy, comparable to CNN-based studies using 64 channels, with significantly lower computational cost and trainable variables reduced to less than 0.25 of those in a Convolutional Neural Network (CNN). …”
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Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models by Ziyang Zhang, Jiancheng Ye

“…Various ML models, including logistic regression, decision trees, and support vector machines, were compared with advanced DL models, including 1D convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. …”
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Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection by Julio Barzola-Monteses, Rosangela Caicedo-Quiroz, Franklin Parrales-Bravo, Cristhian Medina-Suarez, Wendy Yanez-Pazmino, David Zabala-Blanco, Maikel Y. Leyva-Vazquez

“…Experiments were conducted in two scenarios: one using a dataset that included 12 variables, and another in which the variables were reduced to those most significantly correlated with cardiovascular disease, i.e., 4 variables; both scenarios with 918 clinical records per variable. …”
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Detection of <i>Helicobacter pylori</i> Infection in Histopathological Gastric Biopsies Using Deep Learning Models by Rafael Parra-Medina, Carlos Zambrano-Betancourt, Sergio Peña-Rojas, Lina Quintero-Ortiz, Maria Victoria Caro, Ivan Romero, Javier Hernan Gil-Gómez, John Jaime Sprockel, Sandra Cancino, Andres Mosquera-Zamudio

“…Moreover, interobserver variability has been well documented in the traditional diagnostic approach, which may further complicate consistent interpretation. …”
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Revolutionizing Lung Segmentation with Machine Learning: A Critical Review of Techniques in Medical Imaging by Momina Aisha, Moazma Ijaz, Nimra Tariq, Sehar Anjum, Sidra Siddiqui, Usman Hashmi

“…Manual lung segmentation by radiologists, while adjustable, is time-consuming and subject to variability. Consequently, automated lung segmentation methods utilizing Machine Learning (ML) and Deep Learning (DL) have emerged as essential alternatives. …”
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PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing by Yiliang Zeng, Na Meng, Jinlin Zou, Wenbin Liu

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Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion by Junwei Ma, Meiru Huo, Jinfeng Han, Yunfeng Liu, Shunfa Lu, Xiaokun Yu

“…This paper proposes a convolutional neural network‐long short‐term memory (CNN‐LSTM) network integration model based on spatio‐temporal feature fusion. …”
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Deep learning model for patient emotion recognition using EEG-tNIRS data by Mohan Raparthi, Nischay Reddy Mitta, Vinay Kumar Dunka, Sowmya Gudekota, Sandeep Pushyamitra Pattyam, Venkata Siva Prakash Nimmagadda

“…In cross-subject validation, the model attains a 55.53% accuracy, highlighting its robustness despite inter-subject variability. The findings illustrate that the proposed graph convolution fusion approach, combined with modality attention, effectively enhances emotion recognition accuracy and stability. …”
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Machine Learning for Chronic Kidney Disease Detection from Planar and SPECT Scintigraphy: A Scoping Review by Dunja Vrbaški, Boban Vesin, Katerina Mangaroska

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Automated classification of chest X-rays: a deep learning approach with attention mechanisms by Burcu Oltu, Selda Güney, Seniha Esen Yuksel, Berna Dengiz

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