Comparison between observer-based and AI-based reading of CBCT datasets: An interrater-reliability study by Dirk Schulze, Lutz Häußermann, Julian Ripper, Thomas Sottong

“…Objective: To assess the performance of human observers and convolutional neural networks (CNNs) in detecting periodontal lesions in cone beam computed tomography (CBCT), a total of 38 datasets were examined. …”
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An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images by Anik Kumar Saha, Muntezar Rabbani, Anika Saba Ibte Sum, M. F. Mridha, Md Mohsin Kabir

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Deep Learning-Based Performance Prediction of Electric Submersible Pumps Under Viscous and Gas–Liquid Flow Conditions by Haiwen Zhu, Hong Yu, Qiang Sun, Qiuchen Wang, Haorong Jing, Rakhymzhan Abdikadyrov

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Bringing Intelligence to SAR Missions: A Comprehensive Dataset and Evaluation of YOLO for Human Detection in TIR Images by Mostafa Rizk, Israa Bayad

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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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Selective Intensity Ensemble Classifier (SIEC): A Triple-Threshold Strategy for Microscopic Malaria Cell Image Classification by Abdulaziz Anorboev, Sarvinoz Anorboeva, Javokhir Musaev, Esanbay Usmanov, Dosam Hwang, Yeong-Seok Seo, Jeongkyu Hong

“…This involves training three separate convolutional neural network models on the same images processed with different pixel-intensity thresholds: original, pixels above 100, and pixels above 200. …”
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VGG-MFO-orange for sweetness prediction of Linhai mandarin oranges by Chun Fang, Runhong Shen, Meiling Yuan, ZhengXu, Wangyi Ye, Sheng Dai, Di Wang

“…In this paper, a new Attention for Orange (AO) attention mechanism and Multiscale Feature Optimization (MFO) feature extraction module are designed and combined with VGG13 convolutional neural network (CNN), innovatively proposed VGG-MFO-Orange CNN model for accurately classifying mandarin oranges with different sweetness. …”
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AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

“…AngleCam is based on pattern recognition with convolutional neural networks and trained with leaf angle distributions obtained from visual interpretation of more than 2500 plant photographs across different species and scene conditions. …”
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Development of a Deep Learning‐Assisted Mobile Application for the Identification of Nematodes Through Microscopic Images by Naseeb Singh, Ashish Kumar Singh, L. K. Dhruw, Simardeep Kaur, S. Hazarika, K. K. Mishra, V. K. Mishra, Laxmi Kant

“…A novel lightweight convolutional neural network (CNN) was developed to identify the nematodes belonging to different trophic groups (Heterorhabditis indica, Meloidogyne incognita, Helicotylenchus, Anguina tritici, and Xiphinema). …”
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Self-Supervised Social Recommendation Algorithm Fusing Residual Networks by WANG Yujie, YANG Zhe

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A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices by Haijie Shang, Shaobin Yu, Yihan Wu, Xu Liu, Jiayuan He, Min Ma, Xiaoxi Zeng, Ning Jiang

“…The model automatically extracts features from ECG signals at different frequencies through multiple convolutional channels, eliminating the need for manual feature extraction before data input. …”
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The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning by Fatemeh Bahrambanan, Meysam Alizamir, Kayhan Moradveisi, Salim Heddam, Sungwon Kim, Seunghyun Kim, Meysam Soleimani, Saeid Afshar, Amir Taherkhani

“…Based on feature selection models, four different scenarios were developed and five, ten, twenty and thirty features selected for designing a more accurate classification paradigm. …”
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Semantic Tokenization-Based Mamba for Hyperspectral Image Classification by Ri Ming, Na Chen, Jiangtao Peng, Weiwei Sun, Zhijing Ye

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Blockchain enabled IoMT and transfer learning for ocular disease classification by Muhammad Adnan Khan, Muhammad Zahid Hussain, Muhammad Farhan Khan, Munir Ahmad, Sagheer Abbas, Tehseen Mazhar, Tariq Shahzad, Mamoon M. Saeed

“…In the proposed work, six different automated convolutional neural network architectures based on the Internet of Medical Things (IoMT) using transfer learning techniques were implemented for the classification of fundus images that can detect ocular diseases. …”
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SlowFast-TCN: A Deep Learning Approach for Visual Speech Recognition by Nicole Yah Yie Ha, Lee-Yeng Ong, Meng-Chew Leow

“…Consequently, there is less temporal information for distinguishing between different viseme classes, leading to increased visual ambiguity during classification. …”
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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

“…Our proposed automated method has four main steps: preprocessing and image augmentation, nodule detection, nodule classification on the basis of ACR-TIRADS, and risk-level stratification and treatment management. We trained different state-of-the-art pretrained convolutional neural networks (CNNs) to choose the best architecture in the detection and classification stage. …”
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3D-CNN with Multi-Scale Fusion for Tree Crown Segmentation and Species Classification by Jiayao Wang, Zhen Zhen, Yuting Zhao, Ye Ma, Yinghui Zhao

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Cimiciato defect detection in hazelnuts: CNN models applied on X-ray images by Andrea Vitale, Matteo Giaccone, Antonio Gaetano Napolitano, Flavia de Benedetta, Laura Gargiulo, Giacomo Mele

“…Currently used methods for identifying insect damages (cimiciato) often rely on visual inspection, external imaging or require destructive testing.This study compared twelve different pretrained Convolutional Neural Network (CNN) architectures applied on hazelnut kernels X-ray radiographs for the automated detection of cimiciato defects.Through an extensive training and validation process, followed by testing on a separate dataset, InceptionV3 architecture showed the best overall balance across all performance metrics, including accuracy, sensitivity, and precision, while Xception demonstrated superior specificity and the lowest false positive rate. …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…Here, we developed and validated STIED, a simple yet powerful supervised DL algorithm combining two convolutional neural networks with temporal (1D time-course) and spatial (2D topography) features of MEG signals inspired from current clinical guidelines. …”
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Deep Models for Stroke Segmentation: Do Complex Architectures Always Perform Better? by Ahmed Soliman, Yalda Zafari-Ghadim, Yousif Yousif, Ahmed Ibrahim, Amr Mohamed, Essam A. Rashed, Mohamed A. Mabrok

“…Recently, several complex architectures, such as vision Transformers and attention-based convolutional neural networks (CNNs), have been introduced for this task. …”
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