COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization
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In this paper , a classification method for computed tomography chest images in the COVID-19 Radiography Database using features extracted by popular Convolutional Neural Networks (CNN) models was presented, and the determination of hyperparameters of Machine Learning (ML) algorithms by Bayesian optimization, and ANN-based image segmentation are the two main contributions.About:
This article is published in Computers in Biology and Medicine.The article was published on 2022-01-01 and is currently open access. It has received 52 citations till now. The article focuses on the topics: Medicine & Convolutional neural network.read more
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Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform
TL;DR: In this article , a normalized convolutional neural network (NCNN) framework with batch normalization strategy is developed for feature extraction and fault identification of hydraulic piston pump, which can accurately and steadily complete the fault classification of hydraulic pump.
Journal ArticleDOI
Machine learning applications for COVID-19 outbreak management
TL;DR: In this paper , the authors employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers, including survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps.
Journal ArticleDOI
A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
Mei-Ling Huang,YuChieh Liao +1 more
TL;DR: LightEfficientNetV2 as discussed by the authors uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV 2, EfficientNet-B0, and EfficientNetsV2 on COVID-19 detection.
Journal ArticleDOI
A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network
Haitham Alsaif,Ramzi Guesmi,Badr M. Alshammari,Tarek Hamrouni,Tawfik Guesmi,A. A. Alzamil,Lamia Belguesmi +6 more
TL;DR: This paper provides an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation and proves that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.
Journal ArticleDOI
Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans
Nillmani,Neeraj Sharma,Luca Saba,Narendra N. Khanna,Mannudeep K. Kalra,Mostafa M. Fouda,Jasjit S. Suri +6 more
TL;DR: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.
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