4.7 Article

Detection and classification of pneumonia using novel Superior Exponential (SupEx) activation function in convolutional neural networks

Related references

Note: Only part of the references are listed.
Article Radiology, Nuclear Medicine & Medical Imaging

MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net

Gurkan Kavuran et al.

Summary: This study aimed to develop and evaluate a fully automatic deep learning system for diagnosing COVID-19 using thoracic CT scans. The hybrid model MTU-COVNet showed an overall accuracy of 97.7% and high diagnostic performance for detecting COVID-19 and CAP, with specificity and sensitivity reaching 98.0% and 98.2% for COVID-19, and 99.1% and 97.1% for CAP.

CLINICAL IMAGING (2022)

Article Computer Science, Artificial Intelligence

KAF plus RSigELU: a nonlinear and kernel-based activation function for deep neural networks

Serhat Kilicarslan et al.

Summary: Activation functions are crucial for neural network architectures to process input information and generate output. This study proposes extended versions of kernel-based activation functions that overcome existing issues and outperform other activation functions on benchmark datasets.

NEURAL COMPUTING & APPLICATIONS (2022)

Article Chemistry, Multidisciplinary

A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings

Mehmet Akif Bulbul et al.

Summary: This study develops an Artificial Neural Network (ANN)-based model to predict risk priorities for reinforced-concrete (RC) buildings in highly seismic-prone regions. The model achieves accurate predictions with maximum efficiency and a success rate of 98% in determining risk priorities.

APPLIED SCIENCES-BASEL (2022)

Article Chemistry, Analytical

Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets

Loris Nanni et al.

Summary: CNNs and other deep learners have become state-of-the-art in medical imaging research. However, the small sample size of many medical data sets hampers performance and leads to overfitting. Building deep CNN ensembles using pre-trained CNNs is a powerful method to overcome this problem. It relies on introducing diversity in the classification workflow, and a recent method is to vary activation functions in CNNs. This study aims to examine the performance of both methods using a large set of activation functions and demonstrates the superiority of this approach in various medical classification tasks.

SENSORS (2022)

Article Computer Science, Information Systems

Detection of COVID-19 using deep learning techniques and classification methods

Cinare Oguz et al.

Summary: This study aims to reduce the duration and amount of COVID-19 transmission by shortening the diagnosis time of patients using Computed Tomography (CT). Deep learning models and classification methods were employed to develop a decision support system for radiologists. By extracting deep features and evaluating their performance, the study found that the combination of ResNet-50 and SVM achieved the best accuracy, F1-score, and AUC value. The high performance of this system suggests its potential as an auxiliary tool for diagnosing COVID-19.

INFORMATION PROCESSING & MANAGEMENT (2022)

Article Multidisciplinary Sciences

An Adaptive Gaussian Kernel for Support Vector Machine

Abdullah Elen et al.

Summary: An adaptive kernel function based on the Gaussian kernel is designed in this study, which performs well in SVM and is compared with traditional linear, polynomial and Gaussian kernels.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2022)

Article Computer Science, Interdisciplinary Applications

Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation

Kang Cheol Kim et al.

Summary: An automatic X-ray image segmentation technique combining deep-learning and level-set methods is proposed for compression fracture detection and evaluation. This structured hierarchical segmentation method utilizes pose-driven learning and M-net to accurately identify lumbar vertebrae and segment individual vertebrae. Fine tuning segmentation is achieved by combining the level-set method with the obtained segmentation results, resulting in accurate and robust identification of each lumbar vertebra.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

Article Computer Science, Artificial Intelligence

TanhExp: A smooth activation function with high convergence speed for lightweight neural networks

Xinyu Liu et al.

Summary: The Tanh Exponential Activation Function (TanhExp) significantly improves the performance of lightweight or mobile neural networks in image classification tasks, displaying simplicity, efficiency, and robustness. Even with noise added and dataset altered, TanhExp's performance remains stable, enhancing network capacity without increasing the network size and requiring only a few training epochs with no extra parameters.

IET COMPUTER VISION (2021)

Article Computer Science, Artificial Intelligence

Logish: A new nonlinear nonmonotonic activation function for convolutional neural network

Hegui Zhu et al.

Summary: In this paper, a new nonlinear nonmonotonic activation function called Logish is proposed, demonstrating better performance in image classification tasks compared to other common activation functions.

NEUROCOMPUTING (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images

Mohammad Salehi et al.

Summary: In this study, an automated convolutional neural network-based transfer learning approach was used to detect pneumonia in pediatric chest radiographs, with different pre-trained models evaluated for performance metrics. Results showed that all proposed models achieved accuracy greater than 83.0% for binary classification, with DenseNet121 model performing the best.

BRITISH JOURNAL OF RADIOLOGY (2021)

Article Biology

A robust real-time deep learning based automatic polyp detection system

Ishak Pacal et al.

Summary: Colorectal cancer is the third most common type of cancer globally, and colonoscopy is the gold standard for screening. Deep learning architectures show promise in detecting polyps, but real-time detection success has not been achieved. By improving the YOLOv4 algorithm structure, the proposed method demonstrates superior performance on public datasets.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Computer Science, Artificial Intelligence

RSigELU: A nonlinear activation function for deep neural networks

Serhat Kilicarslan et al.

Summary: In this study, novel RSigELU activation functions were proposed to address the issues of vanishing gradient and negative region problems, demonstrating better performance across various activation regions.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Computer Science, Interdisciplinary Applications

A transfer learning method with deep residual network for pediatric pneumonia diagnosis

Gaobo Liang et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

Article Health Care Sciences & Services

An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

Okeke Stephen et al.

JOURNAL OF HEALTHCARE ENGINEERING (2019)

Article Computer Science, Artificial Intelligence

Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder

Kemal Adem et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Computer Science, Artificial Intelligence

Defect detection of seals in multilayer aseptic packages using deep learning

Kemal Adem et al.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES (2019)

Article Biochemistry & Molecular Biology

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Daniel S. Kermany et al.

Article Multidisciplinary Sciences

A New Version of Spherical Magnetic Curves in the De-Sitter Space S12

Selcuk Bas

SYMMETRY-BASEL (2018)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Immunology

Laboratory Methods for Determining Pneumonia Etiology in Children

Niranjan Bhat et al.

CLINICAL INFECTIOUS DISEASES (2012)