An approach to arousal disorder classification using deformable convolution and adaptive multiscale features in EEG signals by Andia Foroughi, Fardad Farokhi, Fereidoun Nowshiravan Rahatabad, Alireza Kashaninia

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HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading by Muhammad Hassaan Ashraf, Hamed Alghamdi

“…This approach can lead to information loss in the initial stages due to limited feature utilization across adjacent layers. To address this limitation, we propose a Hierarchical Features Fusion Convolutional Neural Network (HFF-Net) within a Diabetic Retinopathy Screening and Grading (DRSG) framework. …”
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Applying a Convolutional Vision Transformer for Emotion Recognition in Children with Autism: Fusion of Facial Expressions and Speech Features by Yonggu Wang, Kailin Pan, Yifan Shao, Jiarong Ma, Xiaojuan Li

“…With advances in digital technology, including deep learning and big data analytics, new methods have been developed for autism diagnosis and intervention. Emotion recognition and the detection of autism in children are prominent subjects in autism research. …”
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Urban Land Use Classification Model Fusing Multimodal Deep Features by Yougui Ren, Zhiwei Xie, Shuaizhi Zhai

“…Subsequently, VGG-16 (Visual Geometry Group 16) is used to extract the image convolutional features of the block units, obtaining the raster structure deep features. …”
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PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification by Teng Wang, Fenglian Li, Jia Yang, Wenhui Jia, Fengyun Hu

“…Each DWT and EMD feature is processed by an independent one-dimensional convolutional neural networks (1D-CNN) branch for targeted extraction. …”
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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. …”
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Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification by Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

“…Conclusions: This framework enables precise early Alzheimer’s disease (AD) diagnosis by integrating multi-scale neuroimaging features, empowering clinicians to optimize patient care through timely and targeted interventions.…”
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Lumbar and pelvic CT image segmentation based on cross-scale feature fusion and linear self-attention mechanism by Chaofan Li, Liping Chen, Qiong Liu, Jinnan Teng

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Multimodal Fall Detection Using Spatial–Temporal Attention and Bi-LSTM-Based Feature Fusion by Jungpil Shin, Abu Saleh Musa Miah, Rei Egawa, Najmul Hassan, Koki Hirooka, Yoichi Tomioka

“…The GSTCAN model uses AlphaPose for skeleton extraction, calculates motion between consecutive frames, and applies a graph convolutional network (GCN) with a CA mechanism to focus on relevant features while suppressing noise. …”
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Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture by Syed Asad Imam, Meng Hee Lim, Ahmed Mohammed Abdelrhman, Iftikhar Ahmad, Mohd Salman Leong

“…Noise and complex design in multistage rotors can mask blade faults in vibration signals, necessitating automated feature extraction and expert diagnosis. This research investigates blade FD, comparing traditional machine learning approaches with a novel hybrid deep learning fused model based on a one‐dimensional (1D) convolutional transformer. …”
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EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection by Alanoud Al Mazroa, Majdy M. Eltahir, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K, Jaehyuk Cho

“…This paper proposes a new deep-learning method called Cascaded Atrous Convolutional Network with Adaptive Weight Fusion (CA-AWFM) for classifying schizophrenia from electroencephalogram (EEG) data that combines cascaded networks with atrous convolutions and an adaptive weight fusion module (AWFM). …”
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Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients by Li Li, Wenjun Ren, Yuying Lei, Lixia Xu, Xiaohui Ning

“…Early prediction is critical for timely intervention, but existing methods are limited by poor accuracy and low clinical applicability. …”
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Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features by Abdullah Baktash, Yashar Sarbaz, Saeed Meshgini, Reza Afrouzian

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Crash severity prediction using a virtual geometry-group-based deep learning approach with images-based feature representation by Nanon Sonnatthanon, Kasem Choocharukul

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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

“…These limitations can lead to omissions, misdiagnoses, or inaccurate segmentations, directly impacting clinical assessment and timely intervention. To address these challenges, this study proposes a two-branch hybrid network combining a convolutional neural network (CNN) with a Transformer framework. …”
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An adaptive filter for anemia screening using deep convolutional neural network by Jose B. Lazaro, Jr., Jennifer C. Dela Cruz, Jocelyn F. Villaverde

“…This study introduces an automated anemia detection system powered by deep convolutional neural networks (DCNNs), CMOS image sensing, Adam optimizer, and multi-scale feature extraction (MSFE) to improve diagnostic precision and accessibility. …”
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Efficient sepsis detection using deep learning and residual convolutional networks by Ahmed S. Almasoud, Ghada Moh Samir Elhessewi, Munya A. Arasi, Abdulsamad Ebrahim Yahya, Menwa Alshammeri, Donia Badawood, Faisal Mohammed Nafie, Mohammed Assiri

“…Third is the hierarchical dilated convolutional block (HDCB), which utilises a stacked dilated deep convolutional architecture for spatial feature context retrieval. …”
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Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records by Mamadou Dia, Ghazaleh Khodabandelou, Syed Muhammad Anwar, Alice Othmani

“…Our proposed model, the multichannel convolutional transformer, integrates the strengths of both convolutional neural networks and transformers. …”
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Adaptive Toeplitz convolution- enhanced classifier for anomaly detection in ECG big data by Lili Wu, Tao Li, Majid Khan Majahar Ali, Chenmin Ni, Ying Tian, Xiaojie Zhou

“…This approach combines inherent ECG features with the symmetry of Toeplitz matrices, effectively extracting features from ECG signals and reducing dimensionality. …”
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Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis by Hayin Tamut, Robin Ghosh, Kamal Gosh, Md Abdus Salam Siddique

“…The CNNs model incorporates convolutional layers for feature extraction, max-pooling for down-sampling, dense layers for classification, and dropout for regularization. …”
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