Article
Biochemical Research Methods
Cesar R. Garcia-Jacas, Luis A. Garcia-Gonzalez, Felix Martinez-Rios, Issac P. Tapia-Contreras, Carlos A. Brizuela
Summary: Research shows that non-handcrafted features outperform handcrafted features in terms of performance, but a performance improvement is achieved when both types of features are merged. Non-handcrafted features have higher information content, while handcrafted features are more important, indicating complementarity between the two types of features.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Chemistry, Analytical
Jaeyong Kang, Zahid Ullah, Jeonghwan Gwak
Summary: The study proposed a method for brain tumor classification using deep features and machine learning classifiers, adopting the concept of transfer learning and pre-trained deep convolutional neural networks. Experimental results demonstrated that an ensemble of deep features can significantly improve performance, with support vector machine outperforming other classifiers on large datasets.
Review
Anatomy & Morphology
Tanzila Saba
Summary: Skin covers the entire body and is the largest organ. Skin cancer, particularly melanoma, is a dreadful disease primarily caused by sensitivity to ultraviolet rays. Various handcrafted and automatic deep learning features have been employed to diagnose skin cancer, with a focus on comparing techniques using different features and exploring clinical features for better detection. Parameters like jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity are compared on benchmark data sets to assess reported techniques.
MICROSCOPY RESEARCH AND TECHNIQUE
(2021)
Article
Computer Science, Information Systems
Lachlan Burne, Chiranjibi Sitaula, Archana Priyadarshi, Mark Tracy, Omid Kavehei, Murray Hinder, Anusha Withana, Alistair McEwan, Faezeh Marzbanrad
Summary: This article proposes a novel technique for automated peristalsis sound detection from neonatal abdominal sound recordings and compares it to various other machine learning approaches. It adopts an ensemble approach that utilizes handcrafted as well as one and two dimensional deep features obtained from Mel Frequency Cepstral Coefficients (MFCCs). The results show that our method provides an accuracy of 95.1% and an Area Under Curve (AUC) of 85.6%, outperforming both the baselines and the recent works significantly. These encouraging results demonstrate that our proposed Ensemble-based Deep Learning model is helpful for neonatologists to facilitate tele-health applications.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Victor Hugo da Silva Muniz, Joao Baptista de Oliveira e Souza Filho
Summary: This paper discusses the importance of music genre in music recommendations and presents a method to improve system performance through the generation of new handcrafted features and feature selection.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Biomedical
Meredith A. Jones, Rowzat Faiz, Yuchen Qiu, Bin Zheng
Summary: This study tested the hypothesis that handcrafted and automated features contain complementary classification information and found that the fusion of these two types of features can improve the performance of computer-aided diagnosis of medical images.
PHYSICS IN MEDICINE AND BIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Jiayi Ma, Xingyu Jiang, Aoxiang Fan, Junjun Jiang, Junchi Yan
Summary: Image matching is a fundamental task in various visual applications, and with the development of deep learning techniques, there has been an increasing number of methods proposed in this field. However, the challenge remains in choosing the suitable method for specific applications and designing image matching methods with superior performance. This comprehensive review and analysis provide insights into classical and latest techniques, and offer prospects for future development in image matching technologies.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2021)
Article
Biology
Sadiq Alinsaif, Jochen Lang
Summary: Alzheimer's disease is a neurodegenerative disease that affects millions of people globally, and early detection is crucial for drug trials. This study utilizes various imaging datasets and feature extraction methods, combining shearlet-based descriptors and deep features to improve classification performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Chemistry, Analytical
Hongzu Li, Pierre Boulanger
Summary: Cardiovascular diseases are a leading cause of death worldwide, and early detection and treatment are crucial. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram with handcrafted features to detect heart anomalies beyond the capabilities of commercial products.
Article
Biology
Betul Ay, Cihan Turker, Elif Emre, Kevser Ay, Galip Aydin
Summary: This paper introduces the characteristics and harm of nasal polyps and proposes a reliable rhinology assistance system for recognizing them. The authors design a new dataset including 80 participants and conduct experiments using machine learning and deep learning algorithms. They find that deep learning algorithms achieve high accuracy in identifying nasal polyps. The research results are significant for supporting clinical decision systems.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biophysics
Guadalupe Garcia-Isla, Federico M. Muscato, Andrea Sansonetti, Stefano Magni, Valentina D. A. Corino, Luca T. Mainardi
Summary: This work presents an ECG classifier for variable leads that integrates deep and classic machine learning features into a single model. The classifier combines a deep branch composed of a modified ResNet with dilation convolutional layers, and a wide branch that integrates 20 cardiac rhythm features. Three different training steps were studied to optimize the classification performance. The results show that the integration of handcrafted features and deep learning can improve the generalization capacity of the network and provide explicit information for ECG signals classification.
PHYSIOLOGICAL MEASUREMENT
(2022)
Article
Computer Science, Information Systems
Ammar Mohammed, Rania Kora
Summary: Deep learning-based models have outperformed classical machine learning models in various text classification tasks in the past decade. However, finding the most suitable deep learning classifier remains a challenge. This study proposes a new meta-learning ensemble method that combines baseline deep learning models using 2-tiers of meta-classifiers to improve classification performance. Experimental results demonstrate that the proposed method significantly enhances the accuracy of baseline deep models and outperforms state-of-the-art ensemble methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Biomedical
Unaiza Sajid, Rizwan Ahmed Khan, Shahid Munir Shah, Sheeraz Arif
Summary: Breast cancer is a major cause of death for women worldwide, but early detection can significantly improve survival rates. The research community is working on developing comprehensive frameworks using artificial intelligence to automate breast cancer detection and classification. The proposed framework in this article combines features extracted from Convolutional Neural Network with handcrafted features to outperform current state-of-the-art methods in breast cancer classification.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Antonio Giovannetti, Gianluca Susi, Paola Casti, Arianna Mencattini, Sandra Pusil, Maria Eugenia Lopez, Corrado Di Natale, Eugenio Martinelli
Summary: This paper introduces a novel Deep-MEG approach which combines image-based representations of MEG data with ensemble classifiers based on deep convolutional neural networks for predicting early signs of Alzheimer's disease. By stacking FC indicators from different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and improve classification accuracy. The proposed Deep-MEG architectures show high accuracy for early prediction of AD conversion, indicating its effectiveness in detecting early alterations in spectral-temporal connectivity profiles and spatial relationships.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Biomedical
Menghui Xiang, Junbin Zang, Juliang Wang, Haoxin Wang, Chenzheng Zhou, Ruiyu Bi, Zhidong Zhang, Chenyang Xue
Summary: This paper proposes a method to transfer heart sound classification into image classification and improves the accuracy by comparing different features and using different algorithms. The results show that using logmel and logpower features can achieve better performance in clinical diagnosis, and transfer learning can enhance the performance of the algorithms.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)