Convolutional Neural Networks: A Survey

Computers, Год журнала: 2023, Номер 12(8), С. 151 - 151

Опубликована: Июль 28, 2023

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are subset AI that have emerged as powerful tool for various tasks including image recognition, speech natural language processing (NLP), and even in the field genomics, where they been utilized classify DNA sequences. This paper provides comprehensive overview CNNs their applications recognition tasks. It first introduces fundamentals CNNs, layers convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), training methods. then discusses several popular CNN architectures such LeNet, AlexNet, VGG, ResNet, InceptionNet, compares performance. also examines when use advantages limitations, recommendations developers data scientists, preprocessing data, choosing appropriate hyperparameters (Hyper_Param), evaluating model further explores existing platforms libraries TensorFlow, Keras, PyTorch, Caffe, MXNet, features functionalities. Moreover, it estimates cost using potential cost-saving strategies. Finally, reviews recent developments attention mechanisms, capsule networks, transfer learning, adversarial training, quantization compression, enhancing reliability efficiency through formal The is concluded by summarizing key takeaways discussing future directions research development.

Язык: Английский

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RanMerFormer: Randomized vision transformer with token merging for brain tumor classification

Neurocomputing, Год журнала: 2024, Номер 573, С. 127216 - 127216

Опубликована: Янв. 5, 2024

Brains are the control center of nervous system in human bodies, and brain tumor is one most deadly diseases. Currently, magnetic resonance imaging (MRI) effective way to tumors early detection clinical diagnoses due its superior quality for soft tissues. Manual analysis MRI error-prone which depends on empirical experience fatigue state radiologists a large extent. Computer-aided diagnosis (CAD) systems becoming more impactful because they can provide accurate prediction results based medical images with advanced techniques from computer vision. Therefore, novel CAD method classification named RanMerFormer presented this paper. A pre-trained vision transformer used as backbone model. Then, merging mechanism proposed remove redundant tokens transformer, improves computing efficiency substantially. Finally, randomized vector functional-link serves head RanMerFormer, be trained swiftly. All simulation obtained two public benchmark datasets, reveal that achieve state-of-the-art performance classification. The applied real-world scenarios assist diagnosis.

Язык: Английский

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A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications

IEEE Access, Год журнала: 2024, Номер 12, С. 94250 - 94295

Опубликована: Янв. 1, 2024

Quality inspection and defect detection remain critical challenges across diverse industrial applications. Driven by advancements in Deep Learning, Convolutional Neural Networks (CNNs) have revolutionized Computer Vision, enabling breakthroughs image analysis tasks like classification object detection. CNNs' feature learning capabilities made through Machine Vision one of their most impactful This article aims to showcase practical applications CNN models for surface various scenarios, from pallet racks display screens. The review explores methodologies suitable hardware platforms deploying CNN-based architectures. growing Industry 4.0 adoption necessitates enhancing quality processes. main results demonstrate efficacy automating detection, achieving high accuracy real-time performance different surfaces. However, limited datasets, computational complexity, domain-specific nuances require further research. Overall, this acknowledges potential as a transformative technology vision applications, with implications ranging control enhancement cost reductions process optimization.

Язык: Английский

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Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm

Computers in Biology and Medicine, Год журнала: 2023, Номер 160, С. 106966 - 106966

Опубликована: Апрель 24, 2023

Язык: Английский

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Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 86, С. 105119 - 105119

Опубликована: Июнь 18, 2023

Язык: Английский

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Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 8, 2025

In the diagnosis and treatment of brain tumors, automatic classification segmentation medical images play a pivotal role. Early detection facilitates timely intervention, significantly improving patient survival rates. This study introduces novel method for automated aiming to enhance both diagnostic accuracy efficiency. Magnetic Resonance (MR) imaging remains gold standard in clinical tumor diagnostics; however, it is time-intensive labor-intensive process. Consequently, integration detection, localization, methods not only desirable but essential. this research, we present framework that enables post-classification feature extraction, allowing first-time multiple types. To improve characterization, applied data augmentation techniques MR developed hierarchical multiscale deformable attention module (MS-DAM). model effectively captures irregular complex patterns, enhancing performance. Following classification, comprehensive process was conducted across large dataset, reinforcing model's role as decision support system. Utilizing Kaggle dataset containing 14 different types with highly similar morphologic structures, validated proposed efficacy. Compared existing multi-scale channel modules, MS-DAM achieved superior accuracy, exceeding 96.5%. presents promising approach tumors imaging, offering significant advancements clinics paving way more efficient, accurate, scalable methodologies.

Язык: Английский

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Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network

Reliability Engineering & System Safety, Год журнала: 2022, Номер 231, С. 108990 - 108990

Опубликована: Ноя. 22, 2022

Язык: Английский

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MEEDNets: Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets

Knowledge-Based Systems, Год журнала: 2023, Номер 280, С. 111035 - 111035

Опубликована: Сен. 28, 2023

Inspired by the biological evolution, this paper proposes an evolutionary synthesis mechanism to automatically evolve DenseNet towards high sparsity and efficiency for medical image classification. Unlike traditional automatic design methods, generates a sparser offspring in each generation based on its previous trained ancestor. Concretely, we use synaptic model mimic evolution asexual reproduction. Each generation's knowledge is passed down descendant, environmental constraint limits size of descendant DenseNet, moving process sparsity. Additionally, address limitation ensemble learning that requires multiple base networks make decisions, propose evolution-based mechanism. It utilises scheme generate highly sparse networks, which can be used as perform inference. This specially useful extreme case when there only single network. Finally, MEEDNets (Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets) consists DenseNet-121s synthesised process. Experimental results show our bio-inspired DenseNets are able drop less important structures compensate increasingly architecture. In addition, proposed outperforms state-of-the-art methods two publicly accessible datasets. All source code study available at https://github.com/hengdezhu/MEEDNets.

Язык: Английский

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YoDenBi-NET: YOLO + DenseNet + Bi-LSTM-based hybrid deep learning model for brain tumor classification

Neural Computing and Applications, Год журнала: 2023, Номер 35(17), С. 12583 - 12598

Опубликована: Март 4, 2023

Язык: Английский

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Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor

STUDIES IN ENGINEERING AND EXACT SCIENCES, Год журнала: 2024, Номер 5(1), С. 19 - 35

Опубликована: Янв. 12, 2024

Brain tumors (BT) are fatal and debilitating conditions that shorten the typical lifespan of patients. Patients with BTs who receive inadequate treatment an incorrect diagnosis have a lower chance survival. Magnetic resonance imaging (MRI) is often employed to assess tumor. However, because massive quantity data provided by MRI, early BT detection complex time-consuming procedure in biomedical imaging. As consequence, automated efficient strategy required. The brain or malignancies has been done using variety conventional machine learning (ML) approaches. manually collected properties, however, provide main problem these models. constraints previously stated addressed fusion deep model for binary classification presented this study. recommended method combines two different CNN (Efficientnetb0, VGG-19) models automatically extract features make use feature’s Cubic SVM classifier model. Additionally, approach displayed outstanding performance various measures, including Accuracy (99.78%), Precision Recall F1-Score on same Kaggle (Br35H) dataset. proposed performs better than current approaches classifying from MRI images.

Язык: Английский

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