7 research outputs found
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis
This paper proposes transferred initialization with modified fully connected
layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a
remarkable result in image classification. However, training a high-performing
model is a very complicated and time-consuming process because of the
complexity of image recognition applications. On the other hand, transfer
learning is a relatively new learning method that has been employed in many
sectors to achieve good performance with fewer computations. In this research,
the PyTorch pre-trained models (VGG19\_bn and WideResNet -101) are applied in
the MNIST dataset for the first time as initialization and with modified fully
connected layers. The employed PyTorch pre-trained models were previously
trained in ImageNet. The proposed model is developed and verified in the Kaggle
notebook, and it reached the outstanding accuracy of 99.77% without taking a
huge computational time during the training process of the network. We also
applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection
dataset and achieved 80.01% accuracy. In contrast, the previous methods need a
huge compactional time during the training process to reach a high-performing
model. Codes are available at the following link:
github.com/dipuk0506/SpinalNe
Reduction of Class Activation Uncertainty with Background Information
Multitask learning is a popular approach to training high-performing neural
networks with improved generalization. In this paper, we propose a background
class to achieve improved generalization at a lower computation compared to
multitask learning to help researchers and organizations with limited
computation power. We also present a methodology for selecting background
images and discuss potential future improvements. We apply our approach to
several datasets and achieved improved generalization with much lower
computation. We also investigate class activation mappings (CAMs) of the
trained model and observed the tendency towards looking at a bigger picture in
a few class classification problems with the proposed model training
methodology. Example scripts are available in the `CAM' folder of the following
GitHub Repository: github.com/dipuk0506/U
Swarm Intelligence in Internet of Medical Things: A Review
Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined