  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/SpinalNet
