Deep transfer learning approaches for Monkeypox disease diagnosis

[1]  Mohammad Shabaz,et al.  Optimizing Deep Learning Model Parameters Using Socially Implemented IoMT Systems for Diabetic Retinopathy Classification Problem , 2023, IEEE Transactions on Computational Social Systems.

[2]  Md. Rayhan Ahmed,et al.  Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms , 2022, ArXiv.

[3]  Ahmed Rimaz Faizabadi,et al.  Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach , 2022, Bioengineering.

[4]  Muhammed Coskun Irmak,et al.  Monkeypox Skin Lesion Detection with MobileNetV2 and VGGNet Models , 2022, 2022 Medical Technologies Congress (TIPTEKNO).

[5]  Mohammad Arafat Hussain,et al.  Can Artificial Intelligence Detect Monkeypox from Digital Skin Images? , 2022, bioRxiv.

[6]  S. Shin,et al.  A Blockchain-Based Privacy Sensitive Data Acquisition Scheme During Pandemic Through the Facilitation of Federated Learning , 2022, 2022 13th International Conference on Information and Communication Technology Convergence (ICTC).

[7]  N. Khodadadi,et al.  Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases , 2022, Mathematics.

[8]  Veysel H. Sahin,et al.  Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application , 2022, Journal of Medical Systems.

[9]  Abdelhameed Ibrahim,et al.  Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm , 2022, Mathematics.

[10]  C. Sitaula,et al.  Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches , 2022, Journal of Medical Systems.

[11]  Shahab S. Band,et al.  Time series-based groundwater level forecasting using gated recurrent unit deep neural networks , 2022, Engineering Applications of Computational Fluid Mechanics.

[12]  Shams Nafisa Ali,et al.  Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study , 2022, ArXiv.

[13]  Q. Phan,et al.  Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection , 2022, Journal of biomedical optics.

[14]  Shahana Akter Luna,et al.  Monkeypox Image Data collection , 2022, ArXiv.

[15]  N. Mahroum,et al.  Attaching a stigma to the LGBTQI+ community should be avoided during the monkeypox epidemic , 2022, Journal of medical virology.

[16]  Jennifer L. Small,et al.  Clinical features and management of human monkeypox: a retrospective observational study in the UK , 2022, The Lancet. Infectious diseases.

[17]  M. Ahsan,et al.  A Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning , 2022, Technologies.

[18]  Ashwini Bhat,et al.  Automatic Identification of Bird Species using Audio/Video Processing , 2022, 2022 International Conference for Advancement in Technology (ICONAT).

[19]  Zahed Siddique,et al.  Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review , 2021, Artif. Intell. Medicine.

[20]  Qiuhong Sun,et al.  NGCU: A New RNN Model for Time-Series Data Prediction , 2021, Big Data Res..

[21]  Ross B. Girshick,et al.  Masked Autoencoders Are Scalable Vision Learners , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  I. Al-Qadi,et al.  Prediction of flexible pavement 3-D finite element responses using Bayesian neural networks , 2021, International Journal of Pavement Engineering.

[23]  Cheng Fan,et al.  Attention-based interpretable neural network for building cooling load prediction , 2021 .

[24]  Subhrapratim Nath,et al.  Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning , 2021, Journal of medical imaging.

[25]  Z. Siddique,et al.  Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME , 2021, Healthcare.

[26]  Sorin Hintea,et al.  ECG signal classification using Convolutional Neural Networks for Biometric Identification , 2021, 2021 44th International Conference on Telecommunications and Signal Processing (TSP).

[27]  Andrew Curtis,et al.  Principles for Evaluation of AI/ML Model Performance and Robustness , 2021, ArXiv.

[28]  Mingxing Tan,et al.  EfficientNetV2: Smaller Models and Faster Training , 2021, ICML.

[29]  A. Chughtai,et al.  Reemergence of Human Monkeypox and Declining Population Immunity in the Context of Urbanization, Nigeria, 2017–2020 , 2021, Emerging infectious diseases.

[30]  D. Garreau,et al.  What does LIME really see in images? , 2021, ICML.

[31]  Rajeev Agrawal,et al.  A Transfer Learning approach for AI-based classification of brain tumors , 2020 .

[32]  Aleix M. Martinez,et al.  Explainable Early Stopping for Action Unit Recognition , 2020, 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020).

[33]  U. Moens,et al.  Monkeypox Virus in Nigeria: Infection Biology, Epidemiology, and Evolution , 2020, Viruses.

[34]  Bao-Liang Lu,et al.  Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks , 2020, Neurocomputing.

[35]  Md Manjurul Ahsan,et al.  Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients , 2020, Symmetry.

[36]  Attila Lengyel,et al.  Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task , 2020, ArXiv.

[37]  L. Su,et al.  Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation , 2020, Journal of medical Internet research.

[38]  Kishor Datta Gupta,et al.  COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities , 2020, Mach. Learn. Knowl. Extr..

[39]  Hal Finkel,et al.  Analyzing Deep Learning Model Inferences for Image Classification using OpenVINO , 2020, 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

[40]  Amin Taheri-Garavand,et al.  Deep learning-based appearance features extraction for automated carp species identification , 2020 .

[41]  Mohsen Yousefi,et al.  An optimized model using LSTM network for demand forecasting , 2020, Comput. Ind. Eng..

[42]  Ali Narin,et al.  Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks , 2020, Pattern Analysis and Applications.

[43]  A. Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific Reports.

[44]  Brian D. Davison,et al.  Impact of ImageNet Model Selection on Domain Adaptation , 2020, 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW).

[45]  August Thio-ac,et al.  A Smartphone-Based Skin Disease Classification Using MobileNet CNN , 2019, International Journal of Advanced Trends in Computer Science and Engineering.

[46]  Dimitris Achlioptas,et al.  Bad Global Minima Exist and SGD Can Reach Them , 2019, NeurIPS.

[47]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[48]  Marcus Liwicki,et al.  A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis , 2019, 2019 International Conference on Document Analysis and Recognition (ICDAR).

[49]  Sebastian Stabinger,et al.  Limitation of capsule networks , 2019, Pattern Recognit. Lett..

[50]  Young Min Kim,et al.  RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[51]  S. S. Chaudhuri,et al.  Skin Disease detection based on different Segmentation Techniques , 2019, 2019 International Conference on Opto-Electronics and Applied Optics (Optronix).

[52]  Ankush Mittal,et al.  Pneumonia Detection Using CNN based Feature Extraction , 2019, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).

[53]  A. Vijayalakshmi,et al.  Deep learning approach to detect malaria from microscopic images , 2019, Multimedia Tools and Applications.

[54]  E. M. Dogo,et al.  A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks , 2018, 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS).

[55]  Roger B. Grosse,et al.  Three Mechanisms of Weight Decay Regularization , 2018, ICLR.

[56]  Joelle Pineau,et al.  A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning , 2018, ArXiv.

[57]  Zijun Zhang,et al.  Improved Adam Optimizer for Deep Neural Networks , 2018, 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS).

[58]  Jing Li,et al.  SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis , 2018, Comput. Medical Imaging Graph..

[59]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[60]  Tomaso A. Poggio,et al.  Theory of Deep Learning IIb: Optimization Properties of SGD , 2018, ArXiv.

[61]  Takuya Akiba,et al.  Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes , 2017, ArXiv.

[62]  Anirban Sarkar,et al.  Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[63]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[64]  Zunlei Feng,et al.  Neural Style Transfer: A Review , 2017, IEEE Transactions on Visualization and Computer Graphics.

[65]  Abhishek Das,et al.  Grad-CAM: Why did you say that? , 2016, ArXiv.

[66]  Carlos Guestrin,et al.  Model-Agnostic Interpretability of Machine Learning , 2016, ArXiv.

[67]  L. Nolen,et al.  Extended Human-to-Human Transmission during a Monkeypox Outbreak in the Democratic Republic of the Congo , 2016, Emerging infectious diseases.

[68]  Marcel Salathé,et al.  Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..

[69]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[70]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[71]  Harm de Vries,et al.  RMSProp and equilibrated adaptive learning rates for non-convex optimization , 2015, ArXiv.

[72]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[73]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[74]  Aaron C. Courville,et al.  Generative adversarial networks , 2014, Commun. ACM.

[75]  Madalina Cosmina Popescu,et al.  Feature extraction, feature selection and machine learning for image classification: A case study , 2014, 2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM).

[76]  Feng Hu,et al.  A Novel Boundary Oversampling Algorithm Based on Neighborhood Rough Set Model: NRSBoundary-SMOTE , 2013 .

[77]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[78]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[79]  Tim Menzies,et al.  Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.

[80]  Salvatore J. Stolfo,et al.  Cost-based modeling for fraud and intrusion detection: results from the JAM project , 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.

[81]  Shun-ichi Amari,et al.  Backpropagation and stochastic gradient descent method , 1993, Neurocomputing.

[82]  Hamurabi Gamboa Rosales,et al.  Convolutional Neural Network for Monkeypox Detection , 2022, UCAmI.

[83]  Zahed Siddique,et al.  Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence , 2021, IEEE Access.

[84]  Bo Tao,et al.  Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes Based on KL Decomposition, MLP and LSTM Network , 2020, IEEE Access.

[85]  Marco Tulio Ribeiro,et al.  “ Why Should I Trust You ? ” Explaining the Predictions of Any Classifier , 2016 .

[86]  I. Damon,et al.  Human monkeypox. , 2014, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[87]  Purushottam W. Laud,et al.  Diagnostic tests , 2020, Bayesian Thinking in Biostatistics.

[88]  L. Khodakevich,et al.  Monkeypox virus: ecology and public health significance. , 1988, Bulletin of the World Health Organization.

[89]  Marco Tulio Ribeiro,et al.  Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier , 2022 .