CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis

TL;DR

This paper introduces a transfer learning approach with modified fully connected layers using pre-trained CNN models for COVID-19 diagnosis, achieving high accuracy with reduced training time.

Contribution

It presents a novel application of pre-trained models with modified layers for COVID-19 detection, demonstrating improved efficiency and accuracy.

Findings

Achieved 99.77% accuracy on MNIST dataset.

Achieved 80.01% accuracy on COVID-19 dataset.

Reduced training time compared to previous methods.

Abstract

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