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A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions
Therefore, DL/ML methods are essential to turn IoT systems protection from simply enabling safe contact between IoT systems to intelligence systems in security.
Federated learning: Applications, challenges and future directions
Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems.
Deep Learning for Medical Image Registration: A Comprehensive Review
Finally, a discussion is provided on the promising future research areas in the field of DL-based medical image registration.
Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review
As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases.
Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review
The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life.
Artificial Neural Network for Cybersecurity: A Comprehensive Review
Moreover, a discussion is provided on the currently prevailing cyber-attacks in IoT and other networks, and the effectiveness of DL methods to manage these attacks.
Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review
The advantages and limitations of different ANN models including spiking neural network (SNN), deep belief network (DBN), convolutional neural network (CNN), multilayer neural network (MLNN), stacked autoencoders (SAE), and stacked de-noising autoencoders (SDAE) are described in this review.
Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper.
Hybrid Deep Learning for Detecting Lung Diseases from X-ray Images
For the case of full dataset, VDSNet exhibits a validation accuracy of 73%, while vanilla gray, vanilla RGB, hybrid CNN and VGG, and modified capsule network have accuracy values of 67. 8%, 69%, 69. 5%, 60. 5% and 63. 8%, respectively.
