Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

“…Further, to improve the network’s representation power, a CNN module called Group Convolutional Deep Localization (GCDL) has been introduced, which effectively integrates three different feature descriptors. …”
Get full text

Oriented R-CNN With Disentangled Representations for Product Packaging Detection by Jiangyi Pan, Jianjun Yang, Yinhao Liu, Yijie Lv

“…Furthermore, targets in varying backgrounds necessitate different receptive fields, which can be dynamically adjusted using different convolutional kernels. …”
Get full text

S²RCFormer: Spatial-Spectral Residual Cross-Attention Transformer for Multimodal Remote Sensing Data Classification by Yifei Xu, Lingming Cao, Jialu Li, Wenlong Li, Yaochen Li, Yingjie Zong, Aichen Wang, Yuan Rao, Shuiguang Deng

“…To verify the effectiveness of the proposed method, extensive experiments are conducted on three benchmark datasets (Trento, MUUFL, Augsburg) using four different modality combinations. The results indicate that the proposed approach shows comparable results to other state-of-the-art methods over different metrics.…”
Get full text

Evaluation of sports teaching quality in universities based on fuzzy decision support system by Kunjian Han, Jian Wan

“…The proposed model intakes different factors, such as training patterns, sessions, time, associated with the teaching sessions. …”
Get full text

Multi-Scale DCNN with Dynamic Weight and Part Cross-Entropy Loss for Skin Lesion Diagnosis by Gaoshuai Wang, Linrunjia Liu, Fabrice Lauri, Amir HAJJAM El Hassani

“…Although present methods often use the multi-branch structure to get more clues, the rigescent methods of cropping zone and fusing branch results fail to handle the instability of the disease zone and the difference in branch results, which leads to improper cropping and degrades Deep Convolutional Neural Networks (DCNN)’s performance. …”
Get full text

Electroencephalogram of Happy Emotional Cognition Based on Complex System of Music and Image Visual and Auditory by Lin Gan, Mu Zhang, Jiajia Jiang, Fajie Duan

“…Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t-test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two-classification algorithm can reach 98.8%, and the average accuracy can reach 83.45%. …”
Get full text

3D CNN Approach for Tennis Movement Recognition Using Spatiotemporal Features of Video by Volodymyr Shymanskyi, Ilona Klymenok

“…Also, based on the results, it can be concluded that the use of 3D models can show good results and that it is worth continuing to experiment with their different types.…”
Get full text

Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation by Nisha Purohit, Chandi Prasad Bhatt

“…This review focuses on analyzing different deep learning architectures and explores their performance when optimized using different optimizers. …”
Get full text

Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery by Robert Popescu, Nantheera Anantrasirichai, Juliet Biggs

“…To tackle these issues, this paper explores the use of unsupervised deep learning on InSAR images for the purpose of identifying volcanic deformation as anomalies. We test three different state‐of‐the‐art architectures, one convolutional neural network Patch Distribution Modeling (PaDiM) and two generative models (GANomaly and Denoising diffusion probabilistic models (DDPM)). …”
Get full text

FruitNet: Lightweight CNN for High-Throughput Image-Based Fruit Yield Estimation by Yadav Kamlesh Kumar, Tandan Gajendra

“…Therefore, in order to ensure that the model is robust to different scenarios the model is trained on a robust dataset involving fruit of different variety, growth stage and under different environmental conditions. …”
Get full text

Contributions of lifestyle, education, and cardiovascular risk factors to the brain age gap by Kostas Stoitsas, Pieter Bakx, Trudy Voortman, Jing Yu, Gennady Roshchupkin, Daniel Bos

“…The brain age gap is the difference between chronological age and the age predicted from Magnetic Resonance Imaging (MRI) brain scans. …”
Get full text

Pixels relationship analysis for extracting building footprints by A. Emelyanov, A. Emelyanov, V. Knyaz, V. Knyaz, V. Kniaz, V. Kniaz, D. Artist

“…The main difference from existing methods is a new regularization method based on compiling a neighborhood matrix for each point belonging to the “building” class. …”
Get full text

Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning by Mohsen Mozafari, Andrew J. Law, Rafik A. Goubran, James R. Green

“…A respiratory signal is estimated from a dynamically cropped thermal video using 3D convolutional neural networks and bi-directional long short-term memory stages. …”
Get full text

Hybrid deep learning model for density and growth rate estimation on weed image dataset by Anand Muni Mishra, Mukund Pratap Singh, Prabhishek Singh, Manoj Diwakar, Indrajeet Gupta, Anchit Bijalwan

“…The evaluation of financial misfortunes and impact due to weeds in farming is a critical perspective of considering which makes a difference in formulating suitable management methodologies against weeds.…”
Get full text

Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models by Hongkun Fu, Jian Lu, Jian Li, Wenlong Zou, Xuhui Tang, Xiangyu Ning, Yue Sun

“…Additionally, the study explores the potential of the Green Normalized Difference Vegetation Index (GNDVI) in yield prediction. …”
Get full text

Optimized AlexNet Pruning for Edge-Based Medical Diagnostics by Yasser A. Amer, Hassan I. Saleh, Omar A. Nasr

“…The results reveal a clear difference between fully connected (FC) and convolutional layers: pruning FC layers substantially reduces memory consumption, while pruning convolutional layers significantly boosts inference speed. …”
Get full text

A Low Complexity Algorithm for 3D-HEVC Depth Map Intra Coding Based on MAD and ResNet by Erlin Tian, Jiabao Zhang, Qiuwen Zhang

“…First, we introduce the Mean Absolute Difference (MAD), which quantifies the dispersion of pixel values around the mean within a given region. …”
Get full text

Calcium Extrusion Pump PMCA4: A New Player in Renal Calcium Handling? by Ellen P M van Loon, Robert Little, Sukhpal Prehar, René J M Bindels, Elizabeth J Cartwright, Joost G J Hoenderop

“…There was no significant difference in serum Ca2+ level or urinary Ca2+ excretion between groups. …”
Get full text

MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba by Jianqiang Zhang, Jing Hou, Qiusheng He, Zhengwei Yuan, Hao Xue

“…The direct use of convolutional downsampling reduces selectivity for different stages of information flow. …”
Get full text

MHAGuideNet: a 3D pre-trained guidance model for Alzheimer’s Disease diagnosis using 2D multi-planar sMRI images by Yuanbi Nie, Qiushi Cui, Wenyuan Li, Yang Lü, Tianqing Deng

“…Additionally, a hybrid 2D slice-level network combining 2D CNN and 2D Swin Transformer is employed to capture the interrelations between the atrophy in different brain structures associated with Alzheimer’s Disease. …”
Get full text