Multi-scale conv-attention U-Net for medical image segmentation by Peng Pan, Chengxue Zhang, Jingbo Sun, Lina Guo

“…The AC module dynamically adjusts the convolutional kernel through an adaptive convolutional layer. …”
Get full text

An XNet-CNN Diabetic Retinal Image Classification Method by CHEN Yu, ZHOU Yujia, DING Hui

“…In this research,a retina image automatic recognition system based on Convolutional Neural Network (CNN) is proposed for the disadvantages of the traditional retina image processing process which is cumbersome and poor in robustness. …”
Get full text

ARO-GNN: Adaptive relation-optimized graph neural networks by Yong Lu, Zhengguo Lin

Get full text

Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function by K. Sivagami Sundari, B. Srutha Keerthi

“…Our method is designed to adapt dynamically to different lighting conditions, ensuring effective image enhancement in both uniformly and non-uniformly illuminated environments. …”
Get full text

FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS by ZHOU Tao, YAO DeChen, YANG JianWei

“…Since the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks （CSRP-CNN） was proposed.The model utilizes channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features，and reduces the complexity and computation to improve the performance； using convolutional block attention module （CBAM），attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to important fault feature information； and progressive convolutional network structure was used in the shallow layer of the network，which will fuse the previous fault feature information fused with the current input to obtain richer feature information.The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University（CWRU）and machinery fault simulator magnum（MFS-MG）.After the noise and ablation tests，it is verified that CSRP-CNN has strong robustness and the effects of CSConv，CBAM and progressive convolutional neural network（PCNN） on the model noise immunity performance.…”
Get full text

Fault diagnosis of rolling bearing based on channel and spatial reconstruction networks by ZHOU Tao, YAO Dechen, YANG Jianwei

“…The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University (CWRU) and machinery fault simulator magnum (MFS-MG). …”
Get full text

SER-DC YOLO for the Detection of Abnormal Cervical Cells by LI Chaowei, YANG Xiaona, ZHAO Siqi, HE Yongjun

“… Due to the complex content of Thin Prep Cytology Test ( TCT) images of cervical cell samples with rich and diverse background colors and a certain degree of natural variation of cervical cells among different women，this poses a great difficulty in the detection of abnormal cervical cells. …”
Get full text

Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning by M. Vimala, SatheeshKumar Palanisamy, Sghaier Guizani, Habib Hamam

“…The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. …”
Get full text

Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model by Alessandro Mengarelli, Andrea Tigrini, Mara Scattolini, Rami Mobarak, Laura Burattini, Sandro Fioretti, Federica Verdini

Get full text

Application of YOLO11 Model with Spatial Pyramid Dilation Convolution (SPD-Conv) and Effective Squeeze-Excitation (EffectiveSE) Fusion in Rail Track Defect Detection by Weigang Zhu, Xingjiang Han, Kehua Zhang, Siyi Lin, Jian Jin

“…First, the conventional convolutional layers in the YOLO (You Only Look Once) 11backbone network were substituted with the SPD-Conv (Spatial Pyramid Dilation Convolution) module to enhance the model’s detection performance on low-resolution images and small objects. …”
Get full text

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. …”
Get full text

MultiSEss: Automatic Sleep Staging Model Based on SE Attention Mechanism and State Space Model by Zhentao Huang, Yuyao Yang, Zhiyuan Wang, Yuan Li, Zuowen Chen, Yahong Ma, Shanwen Zhang

“…The MultiSEss architecture utilizes a multi-scale convolution module to capture signal features from different frequency bands and incorporates a Squeeze-and-Excitation attention mechanism to enhance the learning of channel feature weights. …”
Get full text

Multimodal feature fusion-based graph convolutional networks for Alzheimer's disease stage classification using F-18 florbetaben brain PET images and clinical indicators. by Gyu-Bin Lee, Young-Jin Jeong, Do-Young Kang, Hyun-Jin Yun, Min Yoon

“…The usage ratio of these different modal data and edge assignment threshold were tuned by setting them as hyperparameters. …”
Get full text

AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes by Na Ma, Na Ma, Na Ma, Yile Sun, Yile Sun, Chenfei Li, Chenfei Li, Zonglin Liu, Zonglin Liu, Haiyan Song, Haiyan Song

“…Next, shared weight parameters are introduced in the head network, and group convolution is applied to achieve a lightweight detection head. …”
Get full text

AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…The MSCBs employ four parallel 1D convolutional layers with different kernel sizes, enabling the model to extract multi-scale features critical for learning patterns in complex environmental data. …”
Get full text

RLK-YOLOv8: multi-stage detection of strawberry fruits throughout the full growth cycle in greenhouses based on large kernel convolutions and improved YOLOv8 by Lei He, Lei He, Lei He, Dasheng Wu, Dasheng Wu, Dasheng Wu, Xinyu Zheng, Xinyu Zheng, Xinyu Zheng, Fengya Xu, Fengya Xu, Fengya Xu, Shangqin Lin, Shangqin Lin, Shangqin Lin, Siyang Wang, Siyang Wang, Siyang Wang, Fuchuan Ni, Fang Zheng

“…Firstly, it utilizes the large kernel convolution network RepLKNet as the backbone to enhance the extraction of features from targets and complex backgrounds. …”
Get full text

Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network by Kristian Stojšić, Dina Miletić Rigo, Slaven Jurković

Get full text

Novel multistage deep convolution neural network-based Parkinson’s disease detection and severity grading of running speech using LSF spectrums for detection and STFT spectrums for... by Rani Kumari, Prakash Ramachandran

“…The dataset is arranged in 10 different combinations that includes 4 binary classification detection problem and 6 multiclass classification severity grading problem and CNN learning experiments are conducted using LSF, STFT and Mel frequency cepstral coefficients (MFCC) spectrums. …”
Get full text

DB-Net: A Dual-Branch Hybrid Network for Stroke Lesion Segmentation on Non-Contrast CT Images by Xiao Jia, He Dong, Jiashu Xu, Yanhong Zhang, Yihua Lan

“…The spatial channel difference learning module with multiscale parallel subnetworks enhances feature interaction between branches. …”
Get full text

Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KD... by Xiangmin Li, Jiamei Chen, Bo Luo, Minyan Xia, Xu Zhang, Hangjia Zhu, Yutian Zhang-Cai, Yongshun Chen, Yang Yang, Yaofeng Wen

“…Background and objective: Diagnosis of breast intraductal proliferative lesions (BIDPLs) in hematoxylin-eosin (HE) images remains a time-consuming and intractable topic because of subjective processes and subtle morphological differences. Convolutional neural networks (CNNs) show great potential for providing objective analysis strategies for HE images. …”
Get full text