Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach by Yazeed Alashban

“…PURPOSE: The purpose of this study was to propose a new computer-assisted two-staged diagnosis system that combines a modified deep learning (DL) architecture (VGG19) for the classification of digital breast tomosynthesis (DBT) images with the detection of tumors as benign or cancerous using the You Only Look Once version 5 (YOLOv5) model combined with the convolutional block attention module (CBAM) (known as YOLOv5-CBAM). …”
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Stroke Detection in Brain CT Images Using Convolutional Neural Networks: Model Development, Optimization and Interpretability by Hassan Abdi, Mian Usman Sattar, Raza Hasan, Vishal Dattana, Salman Mahmood

“…The CNN architecture comprises three convolutional blocks followed by dense layers optimized through hyperparameter tuning to maximize performance. …”
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An improved XAI-based DenseNet model for breast cancer detection using reconstruction and fine-tuning by Md. Alamin Talukder

“…The key novelty of this work lies in the strategic enhancement of DenseNet with BN-ReLU-Conv and Block-End layers, along with optimized fine-tuning techniques, which improve feature extraction and classification accuracy. …”
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Towards lightweight model using non-local-based graph convolution neural network for SQL injection detection by Duc-Chinh Nguyen, Manh-Hung Ha, Manh-Tuan Do, Oscal Tzyh-Chiang Chen

“…We introduce three graph CNN models, including a graph classification model with a two-layer Graph Convolutional Network (GCN), a graph classification model leveraging a non-local graph convolution derived from a 1x1 convolution, supplanting the original 1x1 convolution, and a modified non-local-block module by substituting the 1x1 convolution layers in the non-local architecture with GCN. …”
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A Relative Attitude Detection Method for Unmanned Aerial Vehicles Based on You Only Look Once Framework by Jingting Qiu, Feifan Yu, Fengrui Xu, Xinmin Chen, Jiqiang Wang

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Detection of Crack Sealant in the Pretreatment Process of Hot In-Place Recycling of Asphalt Pavement via Deep Learning Method by Kai Zhao, Tianzhen Liu, Xu Xia, Yongli Zhao

“…Furthermore, the DRBNCSPELAN (Dilated Reparam Block with Cross-Stage Partial and Efficient Layer Aggregation Networks) module is introduced to ensure efficient information flow, and a lightweight shared convolution (LSC) detection head is developed. …”
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RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8 by Yifan Wei, Jun Tao, Wenjun Wu, Donghua Yuan, Shunzhi Hou

“…Firstly, a combination of hybrid inflated convolution and RefConv is used to redesign the lightweight Ref-Dilated convolution block, which reduces the model computation. Second, a new feature pyramid network fusion module, the Hybrid Bridge Feature Pyramid Network (HBFPN), is designed to fuse the deep features with the high-level features, as well as the features of the current layer, to improve the feature extraction capability for fuzzy objects. …”
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Signal Detection in Intelligent Reflecting Surface-Assisted NOMA Network Using LSTM Model: A ML Approach by Haleema Sadia, Hafsa Iqbal, Syed Fawad Hussain, Nasir Saeed

“…Further, to optimize the phase shifts of IRS, we exploit a low complexity iterative solution using the element-wise block coordinate descent (EBCD) method. Monte Carlo simulations are performed to analyze the performance of the proposed scheme, and the findings show a considerable improvement in channel estimation and signal detection using the LSTM based IRS-NOMA receiver. …”
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YOLO-LSD: A Lightweight Object Detection Model for Small Targets at Long Distances to Secure Pedestrian Safety by Ming-An Chung, Sung-Yun Chai, Ming-Chun Hsieh, Chia-Wei Lin, Kai-Xiang Chen, Shang-Jui Huang, Jun-Hao Zhang

“…The proposed model integrates the C3C2 and the new Efficient Layer Aggregation Network - Convolutional Block Attention Module(ELAN-CBAM) modules to improve the efficiency of feature extraction while reducing computational overhead. …”
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RFAG-YOLO: A Receptive Field Attention-Guided YOLO Network for Small-Object Detection in UAV Images by Chengmeng Wei, Wenhong Wang

“…Additionally, we introduced a Scale-Aware Feature Amalgamation (SAF) component prior to the detection head of RFAG-YOLO. This component employs a scale attention mechanism to dynamically weight features from both higher and lower layers, facilitating richer information flow and significantly improving the model’s robustness to complex backgrounds and scale variations. …”
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Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…The combination of these architectures enhances both the efficiency and classification performance of the model, enabling more accurate detection of retinal disorders from OCT images. Additionally, SE blocks increase the representational ability of the network by adaptively recalibrating per-channel feature responses.…”
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Weld-CNN: Advancing non-destructive testing with a hybrid deep learning model for weld defect detection by Ngo Thi Hoa, Tang Ha Minh Quan, Quoc Bao Diep

“…We propose Weld-CNN, a hybrid convolutional neural network that combines sequential convolutional layers with parallel blocks to effectively extract both low-level and high-level features from X-ray images. …”
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A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…This module incorporates non-local operations under graph convolution domain to deeply explore high-order relationships between adjacent layers, while utilizing depth-wise separable convolution blocks to significantly reduce computational cost. …”
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DermaTransNet: Where Transformer Attention Meets U-Net for Skin Image Segmentation by Anum Abdul Salam, Muhammad Usman Akram, Muhammad Haroon Yousaf, Babar Rao

“…The analysis and detection of diseases heavily rely on identifying the penetration of biomarkers within each layer, thus highlighting the significance of accurate segmentation of each layer in disease diagnosis. …”
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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…An early detection of tomato leaf diseases is crucial for ensuring high-quality and abundant crop production. …”
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Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images by Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan, Mehmet Baygin, Turker Tuncer, U. R. Acharya

“…This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. …”
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Data-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer by Naohiro Masuda, Keiko Ono, Daisuke Tawara, Yusuke Matsuura, Kentaro Sakabe

“…Specifically, these include the data-efficient model, which deepens the hierarchical encoder by adding convolution layers to transformer blocks and increases feature map resolution within transformer blocks, and the FPN-based model, which enhances the decoder through a Feature Pyramid Network (FPN) and attention mechanisms. …”
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FGS-YOLOv8s-seg: A Lightweight and Efficient Instance Segmentation Model for Detecting Tomato Maturity Levels in Greenhouse Environments by Dongfang Song, Ping Liu, Yanjun Zhu, Tianyuan Li, Kun Zhang

“…The model incorporates a novel SegNext_Attention mechanism at the end of the backbone, while simultaneously replacing Bottleneck structures in the neck layer with FasterNet blocks and integrating Gaussian Context Transformer modules to form a lightweight C2f_FasterNet_GCT structure. …”
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PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion by Heqi Yang, Junming Dong, Cancan Wang, Zhida Lian, Hui Chang

“…First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. The ConvNeXtBlock with inverted bottleneck is introduced in the P4 layer, greatly improving small-target feature capture and semantic understanding. …”
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Performance Characteristics of the Battery-Operated Silicon PIN Diode Detector with an Integrated Preamplifier and Data Acquisition Module for Fusion Particle Detection by Allan Xi Chen, Benjamin F. Sigal, John Martinis, Alfred YiuFai Wong, Alexander Gunn, Matthew Salazar, Nawar Abdalla, Kai-Jian Xiao

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