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Article
Computer Science, Artificial Intelligence
Mustafa Ghaderzadeh et al.
Summary: A deep learning-based model utilizing DenseNet201 achieved the best performance in diagnosing and classifying acute lymphoblastic leukemia subtypes. The model demonstrated high accuracy, sensitivity, and specificity of 99.85%, 99.52%, and 99.89% respectively. This proposed method could assist in distinguishing ALL from benign cases and determining appropriate treatment protocols.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Juan E. Arco et al.
Summary: The study introduces a classification framework for medical image diagnosis based on sparse coding, which successfully distinguishes between different pathological types and achieves excellent performance in a real context.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Information Systems
Abhinav Dhere et al.
Summary: Deep learning methods have shown promise in automatic detection of Covid-19 from Chest X-Ray images, but incorporating explainability remains relatively unexplored. This study introduces a hierarchical classification approach using a novel attention architecture for separating normal, non-covid pneumonia, and covid cases with consistent explanations. Through cross-validation, the proposed method achieves competitive accuracy rates and provides clear explanations that align with expert annotations.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Jinjun Tang et al.
Summary: This study introduces a new forecasting framework for accurately predicting traffic flows on urban roads. By integrating different neural network layers and structures, accurate predictions of traffic volume are achieved and validated in the practical system in Changsha.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yu Hua et al.
Summary: This paper proposes a semi-supervised method that narrows the gap between semi-supervised and fully supervised models by utilizing unlabeled data and establishing contrastive relationships between feature representation vectors through supervised contrastive learning. It overcomes data misuse and underutilization in semi-supervised frameworks, enhancing performance.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Javier De Lope et al.
Summary: Speech emotion recognition (SER) is a significant research area in human-machine interaction, and this paper proposes a novel approach combining a time-distributed convolutional neural network (TD-CNN) and a long short-term memory (LSTM) network. The proposed hybrid architecture achieves high recognition accuracy on a publicly distributed database for SER benchmarking, outperforming state-of-the-art deep learning models and conventional machine learning techniques.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Diego Castillo-Barnes et al.
Summary: Spatial normalization allows quantitative comparison of brain scans and the quantification of bias introduced by nonlinear transformations is important for studying neurological disorders. Nonlinear transformations can alter diagnosis outcomes and introduce artificial information.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Gexiang Zhang et al.
Summary: This paper introduces the application of biological neural systems and artificial neural networks in classification tasks, and proposes a novel type of spiking neural P system (LSN P system) to address classification problems. The LSN P system has a flexible structure and high classification performance, designed by mimicking the structure and behavior of biological cells. Experimental results demonstrate the feasibility and effectiveness of the proposed system, showing promising performance in solving real-world classification problems.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Junjian Li et al.
Summary: The study introduces a self-supervised framework based on clustering, which trains the network by learning representations and using pseudo-labels, aiming to reduce the need for data annotation.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Interdisciplinary Applications
Jiatai Lin et al.
Summary: This paper proposes a super lightweight module called PDBL that can improve the classification performance of any well-trained classification backbone without retraining. By constructing a multi-resolution image pyramid and extracting multi-scale deep-broad features, PDBL can steadily improve tissue-level classification performance.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Chemistry, Multidisciplinary
Nada M. Sallam et al.
Summary: The objective of this study is to enhance machine-learning classifiers for the detection of Acute Lymphoblastic Leukemia as benign or malignant using the grey wolf optimization algorithm in feature selection. The model achieved a best accuracy of 99.69%, sensitivity of 99.5%, and specificity of 99% after using the grey wolf optimization algorithm. Comparative results with other classification techniques were included to ensure the effectiveness of the proposed model.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Luis Roda-Sanchez et al.
Summary: Industry 4.0 aims to enhance digitalization in industrial environments through a combination of technology and human factors. The testing of a system utilizing IoT, big data, and human-robot collaboration has demonstrated its capabilities in real-time performance, success rate, flexibility, and scalability.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Rajarshi Bandyopadhyay et al.
Summary: This study proposes a two-stage pipeline for detecting COVID-19 from CT scan images, utilizing a Convolutional Neural Network model and a meta-heuristic algorithm. Experimental results show that the proposed approach outperforms other algorithms in terms of accuracy and feature selection.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Alison O'Shea et al.
Summary: EEG interpretation in preterm infants poses challenges due to limited experts and differences in EEG morphology compared to term infants. Developing specific seizure detection algorithms for preterm infants is difficult due to limited annotated data. Novel DL architectures show promising results for accurately predicting seizures in preterm infants with minimal annotated data.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Multidisciplinary Sciences
Hana Storkanova et al.
Summary: According to the study, plasma levels of Hsp90 are increased in patients with systemic sclerosis compared to healthy controls, and are associated with increased inflammatory activity, worsened lung function, and extent of skin involvement in patients with diffuse cutaneous systemic sclerosis. In patients with systemic sclerosis-associated interstitial lung disease treated with cyclophosphamide, baseline Hsp90 levels can predict changes in DLCO over 12 months of therapy.
SCIENTIFIC REPORTS
(2021)
Article
Mathematical & Computational Biology
Yuteng Xiao et al.
Summary: Early diagnosis and intervention of pathological brains is crucial for controlling, extending lives, and potentially curing patients. This study presents a new approach, TReC, for detecting brain diseases using MRI, achieving excellent performance in both multi-class and two-class tasks, which is essential for early detection and intervention of brain diseases.
FRONTIERS IN NEUROINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Yanna Zhao et al.
Summary: A novel seizure detection method based on graph attention network (GAT) is proposed to effectively utilize the positional relationships between different EEG signals and address the data imbalance issue. Experimental results show that the proposed method achieves high accuracy, sensitivity, and specificity.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Peizhen Peng et al.
Summary: This paper introduces a novel patient-specific method for predicting epileptic seizures, combining Fourier Neural Network (FNN) and Convolutional Neural Network (CNN) for efficient and practical results. By utilizing multi-layer modules and a deep neural network, the prediction of epileptic signals achieves high accuracy and sensitivity.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ahmet Saygili
Summary: The global health crisis caused by the COVID-19 outbreak since December 2019 has led health authorities worldwide to focus on reducing the virus spread rate and seeking rapid and accurate detection methods. This study investigates the contribution of machine learning and image processing in detecting COVID-19 from medical imaging to support early diagnosis and end the pandemic as soon as possible.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Wenyong Zhu et al.
Summary: The DA-MIDL model utilizes Patch-Nets to extract discriminative features from sMRI patches, attention multi-instance learning pooling operation to balance contributions of each patch, and an attention-aware global classifier to learn integral features and make AD-related classification decisions. The model outperforms several state-of-the-art methods in identifying pathological locations and achieving classification accuracy and generalizability.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Artificial Intelligence
Mehmet Akif Ozdemir et al.
Summary: Epilepsy is a common brain disorder worldwide, and EEG recordings are often used to detect and monitor seizures. A novel method combining Fourier-based Synchrosqueezing Transform (SST) and Convolutional Neural Network (CNN) has been proposed for detecting and predicting seizure segments, showing high precision and accuracy rates.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhu et al.
Summary: This paper introduces a novel approach for medical image arbitrary-scale super-resolution, MIASSR, which combines meta-learning and generative adversarial networks (GANs) to achieve super-resolution of medical images at any scale of magnification in (1, 4]. MIASSR shows comparable fidelity performance and the best perceptual quality with the smallest model size compared to state-of-the-art SISR algorithms on single-modal and multi-modal MR brain images. In addition, transfer learning enables MIASSR to handle super-resolution tasks of new medical modalities, with the potential to become a foundational pre-/post-processing step in clinical image analysis tasks.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yu Xue et al.
Summary: This paper introduces a novel NAS algorithm based on multi-objective modeling for designing accurate CNNs with small structures. The proposed algorithm utilizes a graph-based representation of solutions, includes novel ad-hoc crossover and mutation operators, and introduces a mechanism to accelerate the evaluation of candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jianwei Zhang et al.
Summary: Neural Architecture Search (NAS) has attracted growing research interest in automatically designing neural architectures. One-shot NAS methods improve search efficiency by training one supernet that synthesizes all candidate architectures, but face challenges in accuracy. This paper proposes a Hierarchically-Ordered Pruning NAS algorithm to dynamically prune the supernet based on proper pruning direction, achieving competitive results on CIFAR10 and ImageNet.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Review
Computer Science, Hardware & Architecture
Jian Wang et al.
Summary: Transfer learning is gaining popularity in medical image processing due to its efficiency and cost-effectiveness. Despite the variety of medical imaging methods available, labeling data remains a challenge, making transfer learning a valuable solution. Research on the application of transfer learning in medical image analysis holds significant importance for the future development of the field.
MOBILE NETWORKS & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Yaqian Liang et al.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2020)
Review
Neurosciences
Hidir Selcuk Nogay et al.
REVIEWS IN THE NEUROSCIENCES
(2020)
Review
Computer Science, Artificial Intelligence
Guilherme Brandao Martins et al.
Article
Engineering, Biomedical
Muhammed Talo et al.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2019)
Article
Computer Science, Artificial Intelligence
Ahmad Hassanpour et al.
Article
Computer Science, Information Systems
Anjan Gudigar et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Article
Computer Science, Artificial Intelligence
KO Stanley et al.
EVOLUTIONARY COMPUTATION
(2002)
