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Diagnosis system for cancer disease using a single setting approach

MULTIMEDIA TOOLS AND applications 2023年第30期82卷 46241-46267页

作者： Bhuyan, Hemanta Kumar Vijayaraj, A. Ravi, Vinayakumar Deemed Univ Vignans Fdn Sci Technol & Res Dept Informat Technol Guntur Andhra Prades India Prince Mohammad Bin Fahd Univ Ctr Artificial Intelligence Khobar 34754 Saudi Arabia

This paper addresses the diagnosis system of cancer disease using a single setting framework. Most of the radiologists and image specialists are identifying the disease in naked eye. When many conventional systems are used to assess or see a patient's disorder condition, it rarely detects the disease all at once in certain situations. Patients are facing difficulties, when the condition of disease is increasing. Thus, this paper focusses the condition of patient seeing the disease image and developed a single setting framework using a convolutional neural network (CNN) architecture with the help of deep learning approaches. The framework contains several deep learning strategies which are used to determine the patient's relevant illness through affected image, such as mass detection using You-Only-Look-Once (YOLO) approach and the crucial aspect of segmentation by full resolution convolutional networks (FrCN). In last the CNN model is considered for classification. This paper is considered to implement our model using breast cancer disease. The different classifiers and cross-validation tests are taken for evaluating validation matrix items. Comparisons of the existing model with the proposed model are made for improving the diagnosis system. For example, the method Inception V3 for accuracy and AUC are 86.77 and 85.89 on MIAS database whereas proposed model got 99.54 and 98.85 on same evaluation items. Our findings show that the proposed diagnostic model outperforms on conventional detection, segmentation, and classification methods. Thus, our diagnosis process worked much better using deep learning and suggested approaches which will help and facilitate the diagnosis of each contaminated region. In each stage of image processing of the infected region, the suggested diagnostics method could support radiologists.

关键词： Mass detection Segmentation Classification Deep neural network Convolutional neural network

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Comparison of Vision Transformer with Convolutional neural networks for Brain Cancer Classification 17

Comparison of Vision Transformer with Convolutional Neural N...

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17th IEEE International Conference on Computer Research and Development, ICCRD 2025

作者： Manali, Dogu Demirel, Hasan Eastern Mediterranean University Electrical and Electronic Engineering Famagusta Cyprus

ISBN: (纸本)9798331531881

Brain cancer is one of the most deadly illnesses. It causes abnormal cells to grow in the brain. Planning for treatment and the prognosis of patients with brain tumors depend greatly on early diagnosis. Brain tumors can have different characteristics, treatments, and forms. Consequently, the process of manually detecting brain tumors is difficult, labor-intensive, and error-prone. Doctors use magnetic resonance imaging to detect those abnormal cells in the brain. With the growth of artificial intelligence, it is possible to diagnose the brain tumor from MIR images. For instance, convolutional neural networks and transformers could be used. The self-Attention mechanism is implemented by transformers, which are models that give each input data component a distinct weight. Transformers have limited applications in image classification tasks because they were originally designed for use in natural language processing applications. Thus far, the majority of image classification research has employed convolutional neural networks. In this paper, six different pretrained convolutional neural networks and a vision transformer are used to classify four distinct brain tumor classes. The models include ResNet50, AlexNet, VGG16, InceptionV3, MobileNetV2, FractalNet, and the Vision Transformer. The goal of this study is to compare the performance of these pretrained convolutional neural network models with that of the vision transformer, demonstrating that transformers can also be effectively applied to image classification tasks. The performance of a vision transformer model shows 84.39% accuracy in the classification problem, which is better than the other six architectures. © 2025 IEEE.

关键词： Convolutional neural networks

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Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation

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IEEE TRANSACTIONS ON image processing 2024年 33卷 5284-5297页

作者： Gao, Junyu Ma, Xinhong Xu, Changsheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst MA Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural networks, current UDA models are opaque and cannot provide promising explanations, limiting their applications in the scenarios that require safe and controllable model decisions. At present, a surge of work focuses on designing deep interpretable methods with adequate data annotations and only a few methods consider the distributional shift problem. Most existing interpretable UDA methods are post-hoc ones, which cannot facilitate the model learning process for performance enhancement. In this paper, we propose an inherently interpretable method, named Transferable Conceptual Prototype Learning (TCPL), which could simultaneously interpret and improve the processes of knowledge transfer and decision-making in UDA. To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. With the learned transferable prototypes, a self-predictive consistent pseudo-label strategy that fuses confidence, predictions, and prototype information, is designed for selecting suitable target samples for pseudo annotations and gradually narrowing down the domain gap. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts. Code is available at https://***/file/d/1b1EHFghiF1ExD-Cn1HYg75VutfkXWp60/view?usp=sharing.

关键词： Prototypes Adaptation models Motorcycles image recognition Decision making Cognition Target recognition Explainable AI transfer learning image classification deep learning

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Transforming urban waste collection inventory: AI-Based container classification and Re-Identification

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WASTE MANAGEMENT 2025年 199卷 25-35页

作者： Galan, Javier Gonzalez, Miguel Moral, Paula Garcia-Martin, Alvaro lvaro Martinez, Jose M. Univ Autonoma Madrid Video Proc & Understanding Lab Madrid 28049 Spain

This work lays the groundwork for creating an automated system for the inventory of urban waste elements. Our primary contribution is the development of, to the best of our knowledge, the first re-identification system for urban waste elements that uses artificial Intelligence and Computer Vision, incorporating information from a classification module and geolocation context to enhance post-processing performance. This re-identification system helps to create and update inventories by determining if a new image matches an existing element in the inventory based on visual similarity or, if not, by adding it as a new identity (new class or new identity of an existing class). Such a system could be highly valuable to local authorities and waste management companies, offering improved facility maintenance, geolocation, and additional applications. This work also addresses the dynamic nature of urban environments and waste management elements by exploring Continual Learning strategies to adapt pretrained systems to new settings with different urban elements. Experimental results show that the proposed system operates effectively across various container types and city layouts. These findings were validated through testing in two different Spanish locations, a "City" and a "Campus", differing in size, illumination conditions, seasons, urban design and container appearance. For the final re-identification system, the baseline system achieves 53.18 mAP (mean Average Precision) in the simple scenario, compared to 21.54 mAP in the complex scenario, with additional challenging unseen variability. Incorporating the proposed post-processing techniques significantly improved results, reaching 74.14 mAP and 71.75 mAP in the simple and complex scenario respectively.

关键词： Waste Container Classification Deep Learning Convolutional neural networks Object Detection Continual Learning Re-identification

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Convolutional neural network is used in target contour extraction

Convolutional neural network is used in target contour extra...

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2024 International Conference on Mechatronic Engineering and artificial Intelligence, MEAI 2024

作者： Bi, Mingxue Lu, Na College of Equipment Engineerings Shenyang Ligong University Liaoning Shenyang110168 China

ISBN: (纸本)9781510689176

Target contour extraction is a key task in the field of image processing, which is of great significance for applications such as image segmentation, object detection, and scene understanding. Traditional methods are often limited by complex scenarios and changing target shapes, and their effects may be unstable. In recent years, with the vigorous development of deep learning technology, Convolutional neural Network (CNN) has shown significant advantages in target contour extraction tasks due to its powerful feature representation and learning *** this paper, the loss function is used, and the loss function mechanism adopted is Softmax, which is especially suitable for multi-classification problems and is widely used in supervised and unsupervised learning scenarios. © 2025 SPIE.

关键词： Convolutional neural networks

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Improved Surface Defect Classification from a Simple Convolutional neural Network by image Preprocessing and Data Augmentation 10th

Improved Surface Defect Classification from a Simple Convolu...

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10th International Work-Conference on the Interplay Between Natural and artificial Computation (IWINAC)

作者： Lopez de la Rosa, Francisco Moreno-Salvador, Lucia Gomez-Sirvent, Jose L. Morales, Rafael Sanchez-Reolid, Roberto Fernandez-Caballero, Antonio Univ Castilla La Mancha Inst Invest Informat Albacete Calle Invest 2 Albacete Spain Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac Ave Espana S-N Albacete Spain Univ Castilla La Mancha Dept Sistemas Informat Ave Espana S-N Albacete Spain

ISBN: (纸本)9783031611360;9783031611377

Convolutional neural networks (CNNs) play an important role in an increasing number of image processing tasks. There is an obvious demand to improve their classification performance and efficiency. Current research in this area tends to focus on developing increasingly complex models and algorithms to achieve this end. However, research into computer vision techniques and data augmentation tends to be neglected. This paper demonstrates that even a very simple CNN model achieves high performance in surface defect classification on the NEU dataset thanks to image preprocessing and data augmentation. The initial F1-score of 0.9646 without image preprocessing increases to 0.9727 when preprocessing is carried out. The simple CNN then achieves an F1-score of 0.9854 after data augmentation.

关键词： Surface defect classification image preprocessing data augmentation convolutional neural network

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HL-Net: Zero-shot image denoising algorithm based on hybrid learning 24

HL-Net: Zero-shot image denoising algorithm based on hybrid ...

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3rd International Conference on artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wang, Yi Luo, Xiaohui School of Computer and Software Engineering Xihua University Sichuan ChengDu China

ISBN: (纸本)9798400707308

Deep neural networks trained on large datasets have achieved good results in image denoising. However, networks trained on specific datasets often have poor generalization, which is not conducive to practical applications. In addition, most denoisers often ignore the detail differences between image texture areas and smooth areas during the denoising process, resulting in the loss of details in texture areas. In this paper, we propose a zero-shot denoising network (HL-Net) with a Texture Noise Attention module (TNA) and a hybrid learning architecture. TNA allows the network to better extract noise information from texture areas without destroying their details, while the hybrid learning architecture uses two denoising methods for image smooth areas and texture areas: content learning LC (learning non-noise information in noisy images) and noise learning LN (learning noise information in noisy images), which can better preserve the image details in complex texture areas while extracting content information from smooth areas of noisy images. Additionally, the loss function proposed for the zero-sample hybrid learning network uses residual loss, consistency loss, and mean loss, enabling the network to better perform content learning and noise learning. Experiments show that our method outperforms most zero-sample denoising methods on various types of synthetic noise datasets and real-world noise datasets. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Advancing brain tumor detection with neurofusion: an innovative CNN-LSTM model featuring a novel activation function

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SERVICE ORIENTED COMPUTING AND applications 2024年 1-19页

作者： Rawat, Usha Rai, C. S. USICT Guru Gobind Singh Indraprastha Univ Delhi India

Deep neural network has achieved remarkable progress in artificial intelligence. By employing hierarchical abstraction, weight sharing, and local receptive fields, CNNs are highly effective at extracting features from spatial data and capturing significant patterns and structures. However, CNNs are inherently designed for grid-like structures, like images, they may have difficulty processing sequential or temporal data and may not be able to deal with temporal dependencies well. The ability of LSTMs (Long Short-Term Memory networks) to capture long-term relationships makes them suitable for processing sequential data;additionally, applying suitable activation functions, like sigmoid and tanh, improves their capacity to model complex temporal patterns. The paper presents an innovative LSTM model that incorporates a newly introduced activation function, termed modified_sigmoid, specifically applied to the input and forget gates, offering potential improvements for capturing and preserving essential features in image data. Following that, the recently introduced LSTM model is applied to datasets including MNIST, fashion_MNIST, and brain tumors in DICOM and NIfTI formats. Fourteen activation functions are compared with the modified_sigmoid function across the datasets MNIST and fashion_MNIST. In the end, a hybrid CNN-LSTM network, integrating the modified_sigmoid function, is employed to detect brain tumors by utilizing the higher-level features. The accuracy and validation results are impressive, showing no signs of overfitting. When there are no channels in the input, the model achieves a perfect 100% accuracy, and overall accuracy is very high at 99.87% extracted by CNN-LSTM.

关键词： Convolutional neural network Sigmoidal gates LSTM Activation functions

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Self-Supervised Spontaneous Latent-Based Facial Expression Sequence Generation

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL processing 2023年 4卷 304-312页

作者： Yap, Chuin Hong Yap, Moi Hoon Davison, Adrian K. Cunningham, Ryan Manchester Metropolitan Univ Dept Comp & Math Manchester M15 6BH England

In this article, we investigate the spontaneity issue in facial expression sequence generation. Current leading methods in the field are commonly reliant on manually adjusted conditional variables to direct the model to generate a specific class of expression. We propose a neural network-based method which uses Gaussian noise to model spontaneity in the generation process, removing the need for manual control of conditional generation variables. Our model takes two sequential images as input, with additive noise, and produces the next image in the sequence. We trained two types of models: single-expression, and mixed-expression. With single-expression, unique facial movements of certain emotion class can be generated;with mixed expressions, fully spontaneous expression sequence generation can be achieved. We compared our method to current leading generation methods on a variety of publicly available datasets. Initial qualitative results show our method produces visually more realistic expressions and facial action unit (AU) trajectories;initial quantitative results using image quality metrics (SSIM and NIQE) show the quality of our generated images is higher. Our approach and results are novel in the field of facial expression generation, with potential wider applications to other sequence generation tasks.

关键词： Faces Mathematical models Markov processes Gold Training Task analysis image sequences Affective computing artificial neural networks self-supervised learning

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Reliable Analog In-Memory Computing with Crossbars: Memristors for Analog neural Computing

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2025年

作者： Alex James

ISBN: (数字)9781638285632

ISBN: (纸本)9781638285625

artificial intelligence (AI) hardware is a growing area of research that focuses on implementing specialized hardware chips designed for machine learning, neural networks, and their applications. The AI hardware and related chips include the design of efficient processors, memory, and dedicated circuits running AI workloads at extreme efficiency and processing speeds. At the heart of neural network implementations, there are models of neurons that are primarily memory functions capable of learning and adapting to new information. Memory is essential for enabling various learning functions and is inherent in all intelligent beings. Memristors as devices, and the systems built with them, have shown to be of great promise for use in analog neural computing. Every attempt to create an energy-efficient CMOS-based general purpose neural network processor that can compete with human intelligence seems to have failed. Memristive systems and devices are compatible and scalable with CMOS technology and show response behavior to stimuli similar to a biological neuron. This has prompted a closer look at memristive systems in academia and industry through the lens of beyond CMOS technologies, algorithms, and applications. In this monograph, in-memory computing is presented with the memristor as the enabling memory element. The practical memristor device faces several challenges when targeting on-chip implementations. Often, there are conductance variabilities of different forms resulting from device-to-device variability, aging, circuit parasitics, read instabilities, various types of noises, and conductance drifts. This variability and how it can be analysed is introduced, along with the concept of super-resolution for compensating errors in analog computing. The application of memristive processing is also shown through echo-state networks for energy-efficient computing and image filtering processing for RF applications.

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