Article
Biology
Maram Mahmoud A. Monshi, Josiah Poon, Vera Chung, Fahad Mahmoud Monshi
Summary: This paper investigates the use of chest X-rays and artificial intelligence for diagnosing COVID-19, optimizing the model to improve accuracy. The proposed CovidXrayNet model achieves a state-of-the-art accuracy of 95.82% on the COVIDx dataset in the three-class classification task.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Chemistry, Analytical
Khamael Al-Dulaimi, Jasmine Banks, Aiman Al-Sabaawi, Kien Nguyen, Vinod Chandran, Inmaculada Tomeo-Reyes
Summary: This paper proposes a method based on multi-class multilayer perceptron technique to classify HEp-2 stained cells into six classes. The method calculates the variation in higher order spectra features of cell shape information by adding a new hidden layer and uses Softmax activation function to calculate the probability of classification. Extensive experimental analysis on datasets from ICPR-2014 and ICPR-2016 competitions demonstrates that the proposed method outperforms existing methods.
Article
Medicine, General & Internal
Yunfei Liu, Pu Chen, Junran Zhang, Nian Liu, Yan Liu
Summary: In this paper, a ternary stream-driven weakly supervised data augmentation classification network (WT-DFN) is proposed to identify lymphoblasts in a fine-grained scale. By using attention cropping and attention erasing, the proposed method improves the classification accuracy of the model and achieves the best performance on public dataset and real clinical dataset.
Article
Computer Science, Information Systems
Halit Bakir, Ayse Nur cayir, Tugba Selcen Navruz
Summary: This study conducted a comprehensive evaluation of the efficiency of different data augmentation techniques in improving the performance of voice classification models. The results showed that voice augmentation techniques are more suitable than image augmentation techniques for improving the performance of voice classification deep learning models.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Analytical
Shaik Jameer, Hussain Syed
Summary: This research proposes a reliable classification model using wearable sensor data, with an ensemble of Deep SqueezeNet (SE) and bidirectional long short-term memory (BiLSTM) optimized by an improved flower pollination optimization algorithm (IFPOA) for feature extraction. The model achieves high accuracies on three benchmark datasets.
Article
Radiology, Nuclear Medicine & Medical Imaging
Andrew S. Nencka, Volkan E. Arpinar, Sampada Bhave, Baolian Yang, Suchandrima Banerjee, Michael McCrea, Nikolai J. Mickevicius, L. Tugan Muftuler, Kevin M. Koch
Summary: This study systematically examines the impacts of hyperparameter selections for RAKI networks and introduces a novel technique for training data generation, which significantly improves network performance. Hyperparameter tuning of reconstruction networks can lead to further improvements in unaliasing performance.
MAGNETIC RESONANCE IN MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Debamita Kumar, Pradipta Maji
Summary: This study introduces a novel approach for automatic recognition of staining patterns in HEp-2 cells, utilizing a Rough-Bayesian model to evaluate descriptor relevance and a support vector machine to predict staining patterns. The proposed method outperforms state-of-the-art methods and significantly improves the accuracy of classifying HEp-2 cell images.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Huan Zhang, Liangxiao Jiang
Summary: Naive Bayes (NB) is a simple, efficient, and effective data mining algorithm. However, its performance is limited by the unrealistic attribute conditional independence assumption and unreliable conditional probability estimation. This study proposes a novel model called fine tuned attribute weighted NB (FTAWNB), which combines fine tuning with attribute weighting to enhance NB's performance by improving both the attribute conditional independence assumption and conditional probability estimation.
Article
Computer Science, Interdisciplinary Applications
Jungsun Yoo, Tae Joon Jun, Young-Hak Kim
Summary: This study addresses two obstacles in ECG classification and achieved promising results by fine-tuning attention maps.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Computer Science, Information Systems
Wasunan Chokchaithanakul, Proadpran Punyabukkana, Ekapol Chuangsuwanich
Summary: Research focuses on the effect of dataset mismatch on chest radiography and proposes lung balance contrast enhancement technique (lung BCET) and augmentation methods suitable for chest radiography. Results show that lung BCET achieves high AUC scores in out-of-domain testing conditions and can be used for data augmentation in both in- and out-of-domain conditions.
Article
Computer Science, Artificial Intelligence
Andre Luiz C. Ottoni, Raphael M. de Amorim, Marcela S. Novo, Dayana B. Costa
Summary: This paper discusses the importance and challenges of using deep learning methods in building construction image classification, and proposes a method for tuning data augmentation hyperparameters to improve classification accuracy. Experimental results show that the recommended hyperparameter configuration achieved high accuracy in both case studies.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Ben Chen, Feiwei Qin, Yanli Shao, Jin Cao, Yong Peng, Ruiquan Ge
Summary: The study proposes a novel method for diagnosing leukemia by classifying white blood cells in bone marrow using the WBC-GLAformer model. The model combines the features of convolutional neural networks and transformers to enrich the features and improve classification accuracy by selecting discriminative regions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar
Summary: This article proposes a novel augmented convolutional model (ACM) for intelligent cross-domain malware analysis, achieving high accuracy in classification and localization performance on multiple malware datasets.
Article
Computer Science, Artificial Intelligence
D. Elhani, A. C. Megherbi, A. Zitouni, F. Dornaika, S. Sbaa, A. Taleb-Ahmed
Summary: In this work, a method is proposed to automatically generate the best CNN design, using a particle swarm optimization algorithm. A novel strategy is developed to determine the updated position of each particle without relying on the velocity equation. Experimental results show that the proposed method can quickly find effective CNN architectures with comparable performance to the best designs currently available.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Bo Zheng, Wanxiang Che
Summary: This study proposes a method of improving cross-lingual language understanding through consistency regularization-based fine-tuning. By penalizing prediction sensitivity to different data augmentations, the method enables task-specific supervision transfer between languages. Experimental results demonstrate significant improvements in various cross-lingual language understanding tasks.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Victor Hugo Andrade Soares, Ricardo J. G. B. Campello, Seyednaser Nourashrafeddin, Evangelos Milios, Murilo Coelho Naldi
KNOWLEDGE AND INFORMATION SYSTEMS
(2019)
Article
Radiology, Nuclear Medicine & Medical Imaging
Larissa Ferreira Rodrigues, Andre Ricardo Backes, Bruno Augusto Nassif Travencolo, Gina Maira Barbosa de Oliveira
Summary: This paper introduces a hybrid model using genetic algorithms and residual convolutional neural networks for ALL prediction, which improves the accuracy of the classifier. The study suggests that computer vision-based approaches have potential applications in leukemia identification.
JOURNAL OF DIGITAL IMAGING
(2022)
Article
Chemistry, Multidisciplinary
Paulo Gustavo Lopes Candido, Jonathan Andrade Silva, Elaine Ribeiro Faria, Murilo Coelho Naldi
Summary: This study aims to improve the accuracy of sequential clustering of data streams by automatically estimating the number of clusters to adapt to evolving and continuous clusters. Three evolutionary algorithms and three novel algorithms based on goodness-of-fit tests are proposed, which achieve the best results in terms of scalability and accuracy.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Larissa Ferreira Rodrigues Moreira, Rodrigo Moreira, Bruno Augusto Nassif Travencolo, Andre Ricardo Backes
Summary: Coronavirus Disease-2019 (COVID-19) caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) presents challenges for diagnosis and treatment. Chest X-rays and CT scans are effective alternatives for detecting and assessing lung damage caused by COVID-19. AI-based diagnostic systems can provide accurate COVID-19 diagnosis by extracting features from X-ray images. However, the use of low-cost devices and smartphones to hold AI models for disease prediction needs further exploration. This paper proposes an AI as a Service Architecture (AIaaS) to enable embedding of trained models on low-cost devices, providing a case study on COVID-19 diagnosis using a low-cost device.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Flavio de Oliveira Silva
Summary: The Internet is crucial for global applications and businesses, but security is a major challenge. The Darknet, a parallel network within the Internet, requires real-time classification due to malicious activities. Our paper proposes a novel approach using CNN and RL techniques for intelligent and adaptive packet sampling rates in high-performance networks. With a TOR traffic prediction accuracy of 99.84%, our method shows successful classification in high-throughput networks.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Leandro Henrique Furtado Pinto Silva, Jocival Dantas Dias Junior, Joao Fernando Mari, Mauricio Cunha Escarpinati, Andre Ricardo Backes
Summary: Unmanned Aerial Vehicles (UAVs) have played a significant role in assisting and optimizing agricultural production. With the ability to capture detailed images at low and medium altitudes, UAVs provide valuable information for analysis. To address the misalignment issue in multi-spectral images, researchers propose training a deep neural network to predict deformation fields and achieve accurate registration between bands.
2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022)
(2022)
Article
Computer Science, Information Systems
Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Pablo Luiz Araujo Munhoz, Everaldo Antonio Lopes, Renato Adriane Alves Ruas
Summary: This paper introduces a new architecture, AgroLens, which enables real-time disease classification on mobile devices, supporting smart farm applications. It uses low-cost and green-friendly devices, making it operational even in areas without internet connectivity.
INTERNET OF THINGS
(2022)
Article
Materials Science, Paper & Wood
Ricardo Rodrigues de Oliveira Neto, Larissa Ferreira Rodrigues, Joao Fernando Mari, Murilo Coelho Naldi, Emerson Gomes Milagres, Benedito Rocha Vital, Angelica de Cassia Oliveira Carneiro, Daniel Henrique Breda Binoti, Pablo Falco Lopes, Helio Garcia Leite
Summary: The study used Deep Learning Algorithm and Convolutional Neural Network to differentiate charcoal origins with high accuracy, especially in comparison with different preprocessing strategies. This method shows promising potential in improving the identification of charcoal origins.
MADERAS-CIENCIA Y TECNOLOGIA
(2021)
Proceedings Paper
Computer Science, Information Systems
Rodrigo Moreira, Larissa Ferreira Rodrigues, Pedro Frosi Rosa, Rui L. Aguiar, Flavio de Oliveira Silva
Summary: This paper introduces the development of network slicing technology to meet different user requirements and efficiently provide customized resources, as well as a method of guiding path-setting agents through convolutional neural networks. Experimental results demonstrate the suitability of convolutional neural networks for enhancing network slicing and guiding NASOR to establish network slices over multiple domains.
35TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2021)
(2021)
Article
Computer Science, Information Systems
Joelson Antonio dos Santos, Talat Iqbal Syed, Murilo C. Naldi, Ricardo J. G. B. Campello, Joerg Sander
Summary: Hierarchical density-based clustering is a powerful tool for data analysis, but its applicability to large datasets is limited by computational complexity. MapReduce is a programming model used to speed up data mining and machine learning algorithms, however, parallelizing hierarchical clustering algorithms with MapReduce is inherently difficult.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Rodrigo Moreira, Larissa Ferreira Rodrigues, Pedro Frosi Rosa, Rui L. Aguiar, Flavio de Oliveira Silva
2020 33RD SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2020)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Larissa Ferreira Rodrigues, Murilo Coelho Naldi, Joao Fernando Mari
2017 30TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI)
(2017)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)