Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog by Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang

“…Our approach combines a convolutional neural network branch for learning morphological features from r -band images with an artificial neural network branch for extracting spectral energy distribution (SED) information. …”
Get full text

Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome by Frederik M Zimmermann, Pim A L Tonino, Arjan Koks, Jesse P A Demandt, Marcel van ’t Veer, Pieter-Jan Vlaar, Thomas P Mast, Konrad A J van Beek, Marieke C V Bastiaansen

“…The aim of this study is to develop and validate a convolutional neural network (CNN)-based model for risk stratification of suspected NSTE-ACS patients and to compare its performance with currently available prehospital diagnostic tools.Methods For this study, an internal training cohort and an external validation cohort were used, both consisting of suspected NSTE-ACS patients. …”
Get full text

Editorial by Teddy Surya Gunawan

“…Healthcare and safety remain pivotal in this issue, with studies delving into early autism screening using federated learning and diabetic retinopathy detection leveraging deep convolutional neural networks. These works underscore the transformative potential of artificial intelligence in improving diagnostic accuracy and protecting sensitive medical data. …”
Get full text

Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). …”
Get full text

Instance Segmentation of Sugar Apple (<i>Annona squamosa</i>) in Natural Orchard Scenes Using an Improved YOLOv9-seg Model by Guanquan Zhu, Zihang Luo, Minyi Ye, Zewen Xie, Xiaolin Luo, Hanhong Hu, Yinglin Wang, Zhenyu Ke, Jiaguo Jiang, Wenlong Wang

“…An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. …”
Get full text

LRA-UNet: A Lightweight Residual Attention Network for SAR Marine Oil Spill Detection by Yu Cai, Jingjing Su, Jun Song, Dekai Xu, Liankang Zhang, Gaoyuan Shen

“…Our model integrates depthwise separable convolutions to reduce feature redundancy and computational cost, while adopting a residual encoder enhanced with the Simple Attention Module (SimAM) to improve the precise extraction of target features. …”
Get full text

Lightweight Explicit 3D Human Digitization via Normal Integration by Jiaxuan Liu, Jingyi Wu, Ruiyang Jing, Han Yu, Jing Liu, Liang Song

“…We propose a lightweight and efficient 3D human reconstruction model that balances reconstruction accuracy and computational cost. Specifically, our model integrates Dilated Convolutions and the Cross-Covariance Attention mechanism into its architecture to construct a lightweight generative network. …”
Get full text

A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…To reduce computational overhead, we designed the E-CSPPC module, which efficiently combines cross-stage partial networks with gated and partial convolutions, combined with cascaded group attention (CGA) and inverted residual mobile block (iRMB), which minimizes model complexity and computational demand and simultaneously strengthens the model’s capacity for feature representation. …”
Get full text

An eXplainable deep learning model for multi-modal MRI grading of IDH-mutant astrocytomas by Hamail Ayaz, Oladosu Oladimeji, Ian McLoughlin, David Tormey, Thomas C. Booth, Saritha Unnikrishnan

“…The study addresses inter-modality heterogeneity using Principal Component Analysis (PCA) while minimizing computational complexity. LAN uses a 3D Convolutional Neural Network (CNN) and a volumetric attention mechanism to extract tumor patterns and classify grades 2 to 4. …”
Get full text

Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Objectives: The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients. …”
Get full text

Minima-YOLO: A Lightweight Identification Method for Lithium Mineral Components Under a Microscope Based on YOLOv8 by Zeyang Qiu, Xueyu Huang, Xiangyu Xu

“…Third, we incorporated GhostConv, a cost-effective downsampling method, as a replacement for standard convolutions. …”
Get full text

Lightweight YOLOv8 for tongue teeth marks and fissures detection based on C2f_DCNv3 by Chunyang Jin, Delong Zhang, Xiyuan Cao, Zhidong Zhang, Chenyang Xue, Yanjun Zhang

“…By integrating the C2f_DCNv3 module, which incorporates Deformable Convolutions (DCN), replace the original C2f module, enabling the model to exhibit exceptional adaptability to intricate and irregular features, such as fine fissures and teeth marks. …”
Get full text

Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review by Mouzzam Husain, Andrew Simpkin, Claire Gibbons, Tanya Talkar, Daniel Low, Paolo Bonato, Satrajit S. Ghosh, Thomas Quatieri, Derek T. O'Keeffe

“…<italic>Goal:</italic> Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. …”
Get full text

LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position–Morphology Matching Mechanism by Yuliang Zhao, Yang Du, Qiutong Wang, Changhe Li, Yan Miao, Tengfei Wang, Xiangyu Song

“…Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. …”
Get full text

NRGS-Net: A Lightweight Uformer with Gated Positional and Local Context Attention for Nighttime Road Glare Suppression by Ruoyu Yang, Huaixin Chen, Sijie Luo, Zhixi Wang

“…Furthermore, we introduced an improved Uformer backbone named LCAtransformer, in which the downsampling layers adopt efficient depthwise separable convolutions to reduce computational cost while preserving critical spatial information. …”
Get full text

Implementation of Chernobyl disaster optimizer based feature selection approach to predict software defects [version 2; peer review: 2 approved, 1 not approved] by Himansu Das, Ajay Kumar Jena, Kunal Anand

“…However, these models have drawbacks like high cost, local optima trap, lower convergence rate, and higher parameter tuning. …”
Get full text

What slows the progress of health systems strengthening at subnational level? A political economy analysis of three districts in Uganda. by Justine Namakula, Xavier Nsabagasani, Ligia Paina, Abigail Neel, Chimwemwe Msukwa, Daniela C Rodriguez, Freddie Ssengooba

“…Specific challenges included inadequate financing, mismatch of resources and targets, convoluted financial flows, as well as unwieldy bureaucratic processes. …”
Get full text

MultiScaleFusion-Net and ResRNN-Net: Proposed Deep Learning Architectures for Accurate and Interpretable Pregnancy Risk Prediction by Amna Asad, Madiha Sarwar, Muhammad Aslam, Edore Akpokodje, Syeda Fizzah Jilani

“…MultiScaleFusion-Net leverages GRU and multiscale convolutions for effective feature extraction. Additionally, TabNet and MLP models are explored to compare interpretability and computational efficiency. …”
Get full text

Deep learning-assisted diagnosis of liver tumors using non-contrast magnetic resonance imaging: a multicenter study by Shihui Zhen, Shihui Zhen, Shihui Zhen, Peng Zhang, Hanxiao Huang, Zhiyu Jiang, Yankai Jiang, Yankai Jiang, Jihong Sun, Liqing Zhang, Mei Ruan, Qingqing Chen, Yujun Wang, Yubo Tao, Weizhi Luo, Ming Cheng, Zhetuo Qi, Wei Lu, Hai Lin, Xiujun Cai

“…This study aims to develop and validate a deep convolutional neural network for the classification of liver lesions using non-contrast MRI.MethodsA total of 50418 enhanced MRI images from 1959 liver tumor patients across three centers were included. …”
Get full text

Automatic identification of clinically important Aspergillus species by artificial intelligence-based image recognition: proof-of-concept study by Chi-Ching Tsang, Chenyang Zhao, Yueh Liu, Ken P. K. Lin, James Y. M. Tang, Kar-On Cheng, Franklin W. N. Chow, Weiming Yao, Ka-Fai Chan, Sharon N. L. Poon, Kelly Y. C. Wong, Lianyi Zhou, Oscar T. N. Mak, Jeremy C. Y. Lee, Suhui Zhao, Antonio H. Y. Ngan, Alan K. L. Wu, Kitty S. C. Fung, Tak-Lun Que, Jade L. L. Teng, Dirk Schnieders, Siu-Ming Yiu, Susanna K. P. Lau, Patrick C. Y. Woo

“…Given its short turn-around-time, minimal demand of expertise, low reagent/equipment costs and user-friendliness, it has the potential to serve as a routine laboratory diagnostic tool after the database is further expanded.…”
Get full text