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International Conference on machine vision and Augmented intelligence, MAI 2022

作者： Hansdah, Malho Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India

ISBN: (纸本)9789819901883

This work aims to design and develop an artificial neural network (ANN) architecture for the classification of cancerous tissue in the lung. A sequential model is used for the machine learning process. ReLU and Sigmoid activation functions have been used to supply weights to the model. The present work encompasses detecting and classifying the tumor cells into four categories. The four types of lung cancer nodules are adenocarcinoma, squamous-cell carcinoma, large-cell carcinoma, and small-cell carcinoma. Computed tomography (CT) and Positron emission tomography (PET) scan DICOM images are used for the classification. The proposed approach has been validated with the subset of the original dataset. A total of 6500 images have been taken in the experiment. The approach is to feed the CT scan images into ANNs and classify the image as the correct type. The dataset is provided by The Cancer Imaging Archive (TCIA). The dataset is titled "A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis." The tumor cells are classified using the ANN architecture with 99.6% of validation accuracy and 4.35% loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Pulmonary Lung Cancer Classification Using Deep Neural Networks

Pulmonary Lung Cancer Classification Using Deep Neural Netwo...

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International Conference on machine vision and Augmented intelligence, MAI 2022

作者： Goswami, Jagriti Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India

ISBN: (纸本)9789819901883

Lung cancer is the leading cause of cancer-related deaths globally. computer-assisted detection (CAD) systems have previously been used for various disease diagnosis and hence can serve as an efficient tool for lung cancer diagnosis. In this paper, we study the problem of lung cancer classification using chest computed tomography (CT) scans and positron emission tomography–computed tomography (PET-CT). A subset of publicly available Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx) is used to train four different deep learning models using transfer learning for classifying three different types of lung cancer: Adenocarcinoma, Small Cell Carcinoma and Squamous Cell Carcinoma, by passing raw nodule patches to the network. The models are evaluated on metrics such as accuracy, precision, recall, F1-score and Cohen’s Kappa score. ROC curves and confusion matrices are also presented to provide a graphical representation of the models’ performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Boundary-Aware Network with Topological Consistency Constraint for Optic Chiasm Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2023年第6期4卷 1504-1513页

作者： Huang, Wei Shu, Xin Wang, Zizhou Chen, Chaoyue Zhang, Yang Zhang, Lei Xu, Jianguo Sichuan University Machine Intelligence Laboratory College of Computer Science Chengdu610065 China Sichuan University Department of Neurosurgery West China Hospital Chengdu610041 China

The optic chiasm is a structure that is easily compressed by the tumors leading to different degrees of visual field defect and visual disturbance. When the optic chiasm is compressed, the segmentation of the optic chiasm from magnetic resonance imaging images is helpful for prognosis prediction and radiotherapy planning. However, owing to the ambiguity of the optic chiasm boundary and the neglect of the anatomical structure consistency, the performance of the existing methods is limited. In this article, a boundary-aware network (BANet) with a topological consistency constraint is proposed for the automated segmentation of the optic chiasm. The BANet constrained by topology loss leverages the complementary information between the boundary feature and the segmentation feature to effectively improve segmentation performance and topological consistency. To evaluate the effectiveness of the proposed method, a real-world specialized optic chiasm segmentation dataset is constructed. The experimental results demonstrate that the proposed method achieves higher segmentation accuracy compared with the state-of-the-art method. © 2020 IEEE.

关键词： Image segmentation

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An improved particle swarm optimization algorithm for task scheduling in cloud computing

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Journal of Ambient intelligence and Humanized Computing 2023年第4期14卷 4313-4327页

作者： Pirozmand, Poria Jalalinejad, Hoda Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Li, Yingqiu Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding071002 China Department of Mathematics and Computer Science Bandar Abbas Branch Islamic Azad University Bandar Abbas Iran Department of Computer Science University of Regina Regina Canada Tehran Iran

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive Learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cloud computing

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SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 969-985页

作者： Jiang, Wei-Bang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai200240 China

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed. © 2010-2012 IEEE.

关键词： Eye movements

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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A Robust Detection Network Utilizing Neural Memory Ordinary Differential Equations for Vertebral Fractures Diagnosis

A Robust Detection Network Utilizing Neural Memory Ordinary ...

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Li, Saiya Ma, Litai Li, Dehan Deng, Yi Yang, Yi Xu, Lei West China School of Public Health Sichuan University Machine Intelligence Laboratory Chengdu China West China Hospital Sichuan University Department of Orthopedics Chengdu China College of Computer Science Sichuan University Machine Intelligence Laboratory Chengdu China

ISBN: (纸本)9798331504755

Clinicians face challenges in diagnosing complex vertebral fractures, relying on a time-consuming manual classification process that hampers standardization. There is an urgent need for automation. Current automated methods encounter significant limitations, including overfitting due to insufficient data, the laborious and subjective nature of data annotation impeding label acquisition, and imbalances in sample numbers compromising classification accuracy across diverse fracture types. To address these challenges, we propose a one-stage, high-accuracy localization, and classification neural network, denoted as DEEP-Spine, empowered by the recent proposed nmODE (neural memory ordinary differential equations). DEEPSpine represents a cutting-edge solution designed to augment nonlinear capabilities, effectively addressing network overfitting and optimizing network training while ensuring computational efficiency. Additionally, we propose an innovative automatic labeling method to alleviate the manual burden on doctors and generate a comprehensive training dataset. Furthermore, we introduce a novel mechanism for constructing a sample set to mitigate the issue of imbalanced sample sizes and enhance classification accuracy. To validate the effectiveness of our approach, we collected CT images of 287 vertebral fracture cases from West China Hospital of Sichuan University and conducted experiments using Intersection over Union (IoU), Accuracy, Precision, Recall, and F1-score metrics on the constructed dataset. The experimental results demonstrate the validity and effectiveness of our model. © 2024 IEEE.

关键词： computerized tomography

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Frequency forensics for deep fake face detection using dual residual networks

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Multimedia Tools and Applications 2025年 1-26页

作者： Sharafudeen, Misaj Chandra S S, Vinod Machine Intelligence Research Laboratory Department of Computer Science University of Kerala Kerala Trivandrum695581 India

The emergence of deepfake technology has reshaped the landscape of multimedia manipulation, challenging the very foundations of truth and authenticity. Deepfake face synthesis has been exploited by malicious actors to impersonate individuals, fabricate evidence, and perpetrate various forms of fraud, eroding trust in digital interactions. The study explores frequency characteristics and authenticity utilizing datasets from generative models- PGGAN, StyleGAN, and Stable Diffusion. Representation Similarity Analysis (RSA) assesses the authenticity of synthesized face imagery against real facial images, highlighting their resemblance with authentic data and the necessity for efficient detection methods. A novel deepfake detection framework is proposed, backboned by Dual Residual Network (DRN) architecture inspired from Residual Networks. This framework extracts residual traces from deepfake content, enabling precise identification. High-frequency components significantly enhance the accuracy of detection models, with minimal misclassification rates, though on an extremely slight margin when compared to low pass filtered and raw images. The proposed DRN model demonstrated exceptional performance across the DFFD and DFF datasets, achieving Equal Error Rates of 0.04% for DFFD PGGAN, and 0.02% for DFFD StyleGAN as well as the Stable Diffusion track in DFF dataset. The study exhibited consistency in the latest generative technique of Stable Diffusion with a marginal AUC gain of 40.89%. Potential scope of the design extends its applicability beyond deepfake face forgery attacks to domains such as healthcare and insurance fraud, even encompassing a wide array of multimedia domains. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： computer forensics

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Design and Implementation of a vision- and Grating-Sensor-Based Intelligent Unmanned Settlement System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2022年第2期3卷 254-264页

作者： Zhang, Hong-Bo Zhou, Yi-Zhong Dong, Li-Jia Lei, Qing Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361000 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition The Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

In this article, a new vision- and grating-sensor-based intelligent unmanned settlement (IUS) system is proposed for convenience stores to automatically recognize the shopping behavior of customers, record their identities, and generate invoices. First, we design a new IUS architecture, which includes a shelf module and exit module. To achieve automatic settlement for each customer, a shopping event detection method is proposed. In this method, a vision-based human pose estimation algorithm is used to detect a human form standing in front of a shelf. The hand actions of each customer are detected by a grating sensor, and an image recognition method based on a convolutional neural network (CNN) is applied to recognize the items in the hands of customers. To reduce the image annotation workload, we propose a semisupervised training method for the recognition network. Based on hand action detection and item recognition, a shopping event recognition method is designed for the system, and a facial image of the customer corresponding to each shopping behavior is captured. Finally, each detected shopping event is added to the invoice of the corresponding customer via a facial recognition method. To verify the effectiveness of the proposed IUS system, we have built a handheld item image dataset and a shopping event dataset for an unmanned convenience store. The experimental results show that the proposed system can accurately recognize shopping behaviors and generate invoices. © 2020 IEEE.

关键词： Sales

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Continual learning with Bayesian model based on a fixed pre-trained feature extractor

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Visual intelligence 2023年第1期1卷 1-14页

作者： Yang, Yang Cui, Zhiying Xu, Junjie Zhong, Changhong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Network Intelligence Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China

Deep learning has shown its human-level performance in various applications. However, current deep learning models are characterized by catastrophic forgetting of old knowledge when learning new classes. This poses a challenge such as in intelligent diagnosis systems where initially only training data of a limited number of diseases are available. In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning built on a fixed pre-trained feature extractor. In this model, knowledge of each old class can be compactly represented by a collection of statistical distributions, e.g., with Gaussian mixture models, and naturally kept from forgetting in continual learning over time. Unlike existing class-incremental learning methods, the proposed approach is not sensitive to the continual learning process and can be additionally well applied to the data-incremental learning scenario. Experiments on multiple medical and natural image classification tasks reveal that the proposed approach outperforms state-of-the-art approaches that even keep some images of old classes during continual learning of new classes. © The Author(s) 2023.

关键词： Bayesian model Continual learning Fixed feature extractor Generative approach

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