Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images by Saad Islam, Ravinesh C. Deo, Prabal Datta Barua, Jeffrey Soar, U. Rajendra Acharya

“…Our methodology includes image preprocessing pipelines for each modality, custom convolutional neural network/ResNet-based architectures for single-modality analysis, and a two-branch fusion network combining fundus and optical coherence tomography feature representations. …”
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THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN WHITE BLOOD CELL CLASSIFICATION BASED ON MICROSCOPIC IMAGES: A SCOPING REVIEW by Annisa Nur Hasanah, Oktafirani Al Sas, Yosua Darmadi Kosen

“… White blood cell (WBC) classification plays a crucial role in hematological diagnosis and is typically performed manually using microscopic images. However, manual analysis is limited by subjectivity and time inefficiency. …”
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S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data by Junyang Xie, Mengyao Zhang, Hao Wu, Anqi Lin, Marcos Adami, Abdul Rashid Mohamed Shariff, Yahui Guo

“…To address these challenges, we propose a spatial-semantic information fusion network (S2-IFNet) integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data. …”
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COVID-19 detection from chest CT images using optimized deep features and ensemble classification by Muhammad Minoar Hossain, Md. Abul Ala Walid, S.M. Saklain Galib, Mir Mohammad Azad, Wahidur Rahman, A.S.M. Shafi, Mohammad Motiur Rahman

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Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning by Qiyuan Tian, Chanon Ngamsombat, Hong-Hsi Lee, Daniel R. Berger, Yuelong Wu, Qiuyun Fan, Berkin Bilgic, Ziyu Li, Dmitry S. Novikov, Els Fieremans, Bruce R. Rosen, Jeff W. Lichtman, Susie Y. Huang

“…This work presents results from multi-beam scanning electron microscopy (EM) data acquired at 4 × 4 × 33 nm3 resolution in a volume of human superficial white matter measuring 200 × 200 × 112 μm3, leveraging automated analysis methods. …”
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An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networks by A.I. Andreev, S.I. Malkovsky, M.O. Kuchma, Y.A. Shamilova

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Brain tumor segmentation using deep learning: high performance with minimized MRI data by Jacky Huang, Banu Yagmurlu, Powell Molleti, Richard Lee, Abigail VanderPloeg, Humaira Noor, Rohan Bareja, Yiheng Li, Michael Iv, Haruka Itakura

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A Unified Surrogate Framework for Data-Driven Reliability Analysis of Mechanical Systems from Low to Multi-DOF by Lun Shao, Alexandre Saidi, Abdel-Malek Zine, Mohamed Ichchou

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NaDyNet: A toolbox for dynamic network analysis of naturalistic stimuliNaDyNet is open-source and freely available at by Junjie Yang, Zhe Hu, Junjing Li, Xiaolin Guo, Xiaowei Gao, Jiaxuan Liu, Yaling Wang, Zhiheng Qu, Wanchun Li, Zhongqi Li, Wanjing Li, Yien Huang, Jiali Chen, Hao Wen, Binke Yuan

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Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details by Haoyu Lan, Bino A. Varghese, Nasim Sheikh-Bahaei, Farshid Sepehrband, Arthur W Toga, Jeiran Choupan

“…While Generative Adversarial Networks (GAN) has been a prominent method for addressing harmonization tasks, GAN-harmonized images suffer from artifacts or anatomical distortions. …”
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Multimodal malware classification using proposed ensemble deep neural network framework by Sadia Nazim, Muhammad Mansoor Alam, Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Su’ud

“…This study presents a state-of-the-art malware analysis framework that employs a multimodal approach by integrating malware images and numeric features for effective malware classification. …”
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Improving Artificial Intelligence–based Microbial Keratitis Screening Tools Constrained by Limited Data Using Synthetic Generation of Slit-Lamp Photos by Daniel Wang, BA, Bonnie Sklar, MD, James Tian, MD, Rami Gabriel, MD, Matthew Engelhard, MD, PhD, Ryan P. McNabb, PhD, Anthony N. Kuo, MD

“…Objective: We developed a novel slit-lamp photography (SLP) generative adversarial network (GAN) model using limited data to supplement and improve the performance of an artificial intelligence (AI)–based microbial keratitis (MK) screening model. …”
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Deep Learning Method for Wetland Segmentation in Unmanned Aerial Vehicle Multispectral Imagery by Pakezhamu Nuradili, Ji Zhou, Guiyun Zhou, Farid Melgani

“…We present an enhanced semantic segmentation algorithm designed for UAV MS imagery, which incorporates thermal infrared (TIR) data to improve segmentation outcomes. Our approach, involving meticulous image preprocessing, customized network architecture, and iterative training procedures, aims to refine wetland boundary delineation. …”
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Study on Large‐Scale Brain Network Abnormalities in Patients With Beta‐Thalassemia by Mingrui Yang, Guowei Chen, Peng Peng, Cheng Tang, Chaotian Luo, Fei Peng, Rong Kong, Chunxia Zhu, Jiatong Liang

“…This study systematically evaluates alterations in both intra‐brain network functional connectivity (FC) and inter‐network functional connectivity (FNC) in β‐TM patients using resting‐state functional magnetic resonance imaging (rs‐fMRI) and independent component analysis (ICA), aiming to uncover the potential mechanisms underlying their neurofunctional impairments. …”
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Automated Bi‐Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension by Marili Niglas, Nicoleta Baxan, Ali Ashek, Lin Zhao, Jinming Duan, Declan O'Regan, Timothy J. W. Dawes, Chen Nien‐Chen, Chongyang Xie, Wenjia Bai, Lan Zhao

“…A data set of 163 short‐axis cine cardiac magnetic resonance scans were collected longitudinally from monocrotaline (MCT) and Sugen‐hypoxia (SuHx) PH rats and used for training a fully convolutional network for automated segmentation. …”
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Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification by Herimanto, Arie Satia Dharma, Junita Amalia, David Largo, Christin Adelia Pratiwi Sihite

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Technical Code Analysis of Geomagnetic Flaw Detection of Suppression Rigging Defect Signal Based on Convolutional Neural Network by Gang Zhao, Changyu Han, Zhongxiang Yu, Zhipan Li, Guoao Yu, Hongmei Zhang, Dadong Zhao, Zhengyi Jiang

“…Firstly, the magnetic induction signal of the suppression rigging defects is collected using CM-801 (Anshan, China), Kalman filtering is used to screen and pre-process the collected data, and the noise reduction data are presented in the form of a cloud image. …”
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A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics by Nitin Rai, Harsh Pathak, Maria Villamil Mahecha, Dennis R. Buckmaster, Yanbo Huang, Paul Overby, Xin Sun

“…Additionally, to perform a comparative test with neural network-based deep learning (DL) architectures, a convolutional neural network-1-dimensional (CNN-1D_Canola) model was developed to train data points and predict canola yield. …”
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A multiomics dataset of paired CT image and plasma cell-free DNA end motif for patients with pulmonary nodules by Mengmeng Zhao, Gang Xue, Bingxi He, Jiajun Deng, Tingting Wang, Yifan Zhong, Shenghui Li, Yang Wang, Yiming He, Tao Chen, Jun Zhang, Ziyue Yan, Xinlei Hu, Liuning Guo, Wendong Qu, Yongxiang Song, Minglei Yang, Guofang Zhao, Bentong Yu, Minjie Ma, Lunxu Liu, Xiwen Sun, Deping Zhao, Dan Xie, Chang Chen, Yunlang She

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Predicting lung cancer bone metastasis using CT and pathological imaging with a Swin Transformer model by Wanling Li, Xin Zou, Jie Zhang, Minghong Hu, Guanfeng Chen, Shanshan Su

“…The model was designed to process high-resolution CT images and digitized histopathological images, with the features extracted independently by two Swin Transformer networks. …”
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