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Multiscale Dynamic Attention Framework for Skin Lesion Segmentation and Classification

International Journal of Imaging Systems and technology 2025年第4期35卷

作者： G. Prince Devaraj V. Vanitha K. Vimala Praveen Kumar Gupta Department of Information Technology Francis Xavier Engineering College Tirunelveli India Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai Tamilnadu India Artificial Intelligence and Data Science Sri Krishna College of Technology Coimbatore Tamilnadu India School of Computer Science and Engineering Galgotias University Greater Noida India

This paper proposes a multitask deep learning framework for simultaneous skin lesion segmentation and classification, tailored for resource-constrained environments. The architecture integrates ShuffleNet as an encoder within a U-Net framework as a decoder to extract features. Using ShuffleNet's depthwise separable convolutions and channel shuffling, the model captures both low-level spatial details and high-level semantic features. To enhance feature representations, the proposed adaptive multiscale dynamic attention network (AMSDAN) is employed at two critical stages: first, to refine feature maps during segmentation by dynamically normalizing features across multiple scales; and second, to refine the segmented output for classification by highlighting discriminative patterns. The proposed work is trained and validated using HAM10000 and ISIC-2017 datasets. Experimental results demonstrate that the framework outperforms state-of-the-art techniques.

关键词： AMSDAN multitask learning ShuffleNet skin lesion classification skin lesion segmentation U-Net

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An Efficient Fusion of Infrared and Visible Images using an Optimized Deep Learning Model with Decomposed Non-Subsampled Shearlet Structural Patch Transform

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Signal Processing: Image Communication 2025年

作者： P. Murugeswari Sonia Jenifer Rayen M. Ramkumar U. Sakthi Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore Tamil Nadu 641032 India Sathyabama Institute of Science and Technology Chennai Tamil Nadu 600119 India Electronics and Communication Engineering Department Sri Krishna College of Engineering and Technology Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu 603203 India

Multimodal data fusion is pivotal in artificial intelligence, with Infrared and Visible image Fusion (IVF) crucial for enhancing target detection. Thus, an innovative model is proposed for IVF, leveraging optimized deep learning-based feature extraction and a novel fusion model. Initially, a normalized depth wise crested porcupine cross-attention model is introduced to extract complementary details from visible and infrared images. Subsequently, the Decomposed Non-subsampled Shearlet Structural Patch Transform (DNSSPT) model is employed to integrate features and generate the fused image. This involves decomposing the extracted infrared and visible images into different parts such as signal intensity, mean intensity, and signal structure utilizing structural patch decomposition. Next, a membership curve role is employed to precisely determine the weight of average intensity module, thereby minimizing artifacts and maintaining the importance of infrared targets. Additionally, sharpening operations are employed to improve the detail layer of both the visible and infrared images, leading to a fused image with higher contrast. Through subjective and objective experimental evaluations, the developed model surpasses existing fusion techniques by achieving minimum efficiencies of 0.0521,0.0698, 0.0681, 0.0832 seconds with a maximum detection accuracy of 92.71%, 96%, 90.89%, 94%across different datasets such as TNO, MSRS, VIFB, and VOT-RGBT.

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Eddy Current Imaging of Titanium Plate Surface Defects Based on Compressed Sensing

Eddy Current Imaging of Titanium Plate Surface Defects Based...

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Far East Forum on Nondestructive Evaluation/Testing: New technology & Application (FENDT)

作者： Zhaoyuan Li Bo Ye Xin Xiong Yidan Zhang Weiquan Deng Jun Bao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Key Laboratory of Artificial Intelligence of Yunnan Province Kunming China Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781665401722

Titanium has excellent comprehensive properties, and its alloys are widely used in aerospace, petrochemical, bioengineering and other industries, among which plate is the most widely used. Before using titanium plate, visual eddy current testing of surface defects is particularly important. However, a large amount of data will be generated in the detection process, most of which are redundant data unrelated to defect imaging. Imaging with this data is not only inefficient, but also a waste of detection resources. In order to solve this problem, this paper proposes an eddy current imaging method of titanium plate surface defects based on compressed sensing. The induced voltage signal in the process of eddy current probe C-scan is sampled at a frequency far lower than that required by Nyquist sampling theorem, which shortens the sampling time and improves the sampling efficiency. The sampled signal is reconstructed by reconstruction algorithm, which can achieve almost the same effect as the original signal. The experimental results show that the proposed method only uses 3 / 8 of the original signal for imaging, and the reconstruction algorithm used in this method has faster reconstruction speed, lower reconstruction error and better reconstruction effect than other mainstream reconstruction algorithms. The use of this method improves the efficiency of the eddy current imaging detection of titanium plate surface defects, and compresses the space occupied by the imaging data. It can not only provide important technical support for optimizing the eddy current detection process of titanium plate surface defects, but also has important significance for the eddy current detection of other metals.

关键词： Surface reconstruction Image edge detection Imaging Titanium Reconstruction algorithms Eddy currents Image reconstruction

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Heterophily-Aware Graph Attention Network

arXiv

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arXiv 2023年

作者： Wang, Junfu Guo, Yuanfang Yang, Liang Wang, Yunhong State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Shen Yuan Honors College Beihang University Beijing100191 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Hebei University of Technology Tianjin300401 China

Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess different labels or features. Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem. In this paper, we first propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i.e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, enabling nodes to acquire appropriate information from distinct neighbors. Then, we propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily, to handle the networks with different homophily ratios. To demonstrate the effectiveness of the proposed HA-GAT, we analyze the proposed heterophily-aware attention scheme and local distribution exploration, by seeking an interpretation from their mechanism. Extensive results demonstrate that our HA-GAT achieves state-of-the-art performances on eight datasets with different homophily ratios in both the supervised and semi-supervised node classification tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Deep learning-based detection of incisal translucency patterns

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Journal of Prosthetic Dentistry 2025年

作者： Shetty, Sthithika Gali, Sivaranjani R, Venkatesh MS, Jeswin MN, Thippeswamy Postgraduate student Department of Prosthodontics and Crown & Bridge Faculty of Dental Sciences M.S. Ramaiah University of Applied Sciences Bangalore India Head Department of Prosthodontics and Crown & Bridge Faculty of Dental Sciences M.S. Ramaiah University of Applied Sciences Bangalore India Graduate student Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning) Ramaiah Institute of Technology Bangalore India Principal Dr. Ambedkar Institute of Technology Bangalore India

Statement of problem: The evaluation of incisal translucency in anterior teeth greatly influences esthetic treatment outcomes. This evaluation is mostly subjective and often overlooked among dental professionals. The application of artificial intelligence-based models to detect the incisal translucency of anterior teeth may be of value to dentists in their restorative dental practice, but studies are lacking. Purpose: The purpose of this study was to assess the accuracy of deep learning models in predicting the translucency patterns of anterior teeth. Material and methods: Approximately 240 Joint Photographic Experts Group (JPEG) images of anterior teeth from participants over 18 years were collected using a smartphone. These images were resized to 224×224 pixels and classified by the presence or absence of translucency. Augmentation techniques enhanced the training dataset, and a 3-model deep learning approach was used: YOLOv5 detected central incisors, Vision Transformers (ViT) identified translucency, and U-Net segmented the translucent areas. The images were split 80 to 20 for training and testing, with performance evaluated using accuracy, precision, recall, F1 score, confusion matrix, and dice scores. Results: YOLOv5 achieved a precision of 1.00 at a confidence threshold of 0.910. The ViT system showed an accuracy of 91.66%, with 58 of 64 images predicting correctly with an F1 score of 94.83%. U-Net segmentation after training with annotated images achieved an accuracy of 91% with a dice score of 0.948. Conclusions: The integration of YOLOv5 for detection, ViT for classification, and U-Net for segmentation demonstrates a comprehensive approach to addressing the classification of incisal translucencies. By leveraging the strengths of deep learning models, high accuracy and precision can be achieved in detecting the incisal translucency patterns of anterior teeth. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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Tcgfusion: A Network for Pet-Mri Fusion Based on Gan and Transformer

SSRN

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SSRN 2022年

作者： Fan, Chao Chen, Zhixiang Lin, Hao Wang, Xiao School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China

We provide an end-to-end unsupervised training model called TCGFusion: a network for PET-MRI fusion based on GAN and transformer. We set the generator to a dual-path layout to assure the gathering of both global and detailed image information. To guarantee the coarse scale picture extraction and prevent the high computational cost, the global path T-Path utilizes Grid-former (a residual grid transformer structure). The detailed path C-Path is DenseNet, and its multi-cascade structure captures image details well. Additionally, we set up a dual adversarial mechanism between the fused image and the source image, creating a dual discriminator to determine the distribution of structural similarity between the fused result and the source image, and using patch-SSIM to reinforce the semantic constraints between them and promote the image details. According to our extensive studies on public datasets, medical image fusion demonstrate the superiority of our TCGFusion compared to the state-of-the-art image fusion algorithms. © 2022, The Authors. All rights reserved.

关键词： Semantics

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Carbon fiber reinforced polymer defect detection using magnetic induction tomography method

Carbon fiber reinforced polymer defect detection using magne...

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Far East Forum on Nondestructive Evaluation/Testing: New technology & Application (FENDT)

作者： Honggui Cao Bo Ye Siqi Luo Jun Bao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Provincial Key Laboratory of Artificial Intelligence Kunming China Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781665401722

Carbon Fiber Reinforced Polymer (CFRP) is widely used in aerospace, military and other fields due to its excellent performance. In order to meet the requirements of rapid detection, location and visualization of defects in carbon fiber composite reinforced polymer. The defect detection method of carbon fiber reinforced polymer based on magnetic induction tomography (MIT) was investigated. MIT of four-layer unidirectional carbon fiber reinforced polymer laminates model was established by COMSOL Multiphysics, a planar electromagnetic sensor array for CFRP laminates was designed, the sensitivity matrix was calculated based on the reciprocity theorem and correction factor P was introduced to eliminate the influence of negative sensitivity on image reconstruction, then Tikhonov regularization algorithm and Landweber algorithm were used for image reconstruction of CFRP laminates defects. Thus, the results show that the designed planar sensor array can effectively detect the defects of CFRP laminates, at the same time, image reconstruction based on improved sensitivity has better effect on defect detection, and the shape and location of defects are more accurate; It shows that MIT is feasible for defect detection of carbon fiber reinforced polymer.

关键词： Optical fiber sensors Sensitivity Transmission line matrix methods Magnetic resonance imaging Tomography Polymers Laminates

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Dfe-Net: Dual-Branch Feature Extraction Network for Enhanced Segmentation in Skin Lesion

SSRN

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SSRN 2022年

作者： Fan, Chao Yang, Litao Lin, Hao Qiu, Yingying School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City China

Skin lesion segmentation is a critical method for extracting pathological information from dermoscopy images, which is of great significance for lesion location, recognition, monitoring and treatment. Due to the varied sizes of lesions, different forms and colors, and blurring boundaries, the present detection methods are unable to accurately anticipate local features, which has an impact on the segmentation accuracy. Therefore, the Dual-branch Feature Extraction Network (DFE-Net) is suggested in this paper. We designed two types of encoders based on the Transformer and Efficient Channel Attention (ECA) module, one for extracting the global feature and another for the local feature, before fusing and decoding. Simultaneously, the skip connections from the Enhanced ECA Feature Extraction Modules to decoders are designed to reduce feature loss during decoding, this will restore the boundaries and local features to the greatest extent possible. We test the model on three publicly available skin lesion datasets: PH2, ISIC-2016, and ISIC-2018. The results show that our model is superior to other previous methods, and the segmentation characteristics are more accurate representations of the actual lesion. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Learning Bidirectional Translation between Descriptions and Actions with Small Paired data

arXiv

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arXiv 2022年

作者： Toyoda, Minori Suzuki, Kanata Hayashi, Yoshihiko Ogata, Tetsuya Faculty of Science and Engineering Waseda University Tokyo169-8050 Japan Artificial Intelligence Laboratories Fujitsu Limited. Kanagawa211-8588 Japan Waseda University Tokyo169-8555 Japan The National Institute of Advanced Industrial Science and Technology Tokyo100-8921 Japan

This study achieved bidirectional translation between descriptions and actions using small paired data from different modalities. The ability to mutually generate descriptions and actions is essential for robots to collaborate with humans in their daily lives, which generally requires a large dataset that maintains comprehensive pairs of both modality data. However, a paired dataset is expensive to construct and difficult to collect. To address this issue, this study proposes a two-stage training method for bidirectional translation. In the proposed method, we train recurrent autoencoders (RAEs) for descriptions and actions with a large amount of non-paired data. Then, we finetune the entire model to bind their intermediate representations using small paired data. Because the data used for pre-training do not require pairing, behavior-only data or a large language corpus can be used. We experimentally evaluated our method using a paired dataset consisting of motion-captured actions and descriptions. The results showed that our method performed well, even when the amount of paired data to train was small. The visualization of the intermediate representations of each RAE showed that similar actions were encoded in a clustered position and the corresponding feature vectors were well aligned. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Photovoltaic Powered Electric Vehicle Using Direct Torque Control and IoT Monitoring

Photovoltaic Powered Electric Vehicle Using Direct Torque Co...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： P. Sathyanathan S. Manikandan D. Chandrakala Raian. VR Ishwarya M.V S. Sujatha Department of Electrical and Electronics Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Meenakshi Sundararajan Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering MVJ College of Engineering Bangalore Karnataka India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

The concept of speed control of a Permanent Magnet Synchronous Motor (PMSM) using Direct Torque Control (DTC) is proposed for use in electric vehicles (EVs). A solar Photo Voltaic (PV) system powers the PMSM, and they are monitored using Internet of Things (IoT). In this configuration, a PMSM serves as the EV's main propulsion engine, with speed control achieved by DTC which provides fast and accurate torque and flux control. By using DTC to accurately control the PMSM speed, EV operation may be more reliable and efficient. This integration lessens the dependence on conventional energy sources and enables the functioning of the EV in a sustainable and ecologically responsible manner. The PV system offers a sufficient and clean source of energy, enhancing the EV's overall performance and range. IoT technology is used to keep monitor the motor speed and data from the PV system. The IoT allows instantaneous information exchange and connection across systems and objects.

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