A deep learning framework for accurate COVID-19 classification in CT-scan images

Abstract

Background

In response to the global COVID-19 pandemic, we have introduced a binary classification model that employs convolutional layers to differentiate between normal cases and COVID-19-infected cases. Our primary aim was to address the urgent need for a highly efficient and accurate diagnostic tool to combat the widespread outbreak of COVID-19.

Methods

To achieve the background, we proposed a convolutional structure that comprises 10 layers in the encoder and 3 dense layers in the decoder. We conducted comprehensive experiments and evaluations using four distinct datasets.

Results

The outcomes of our study consistently demonstrated remarkable performance, with our proposed model achieving an accuracy of 89.00 %, a sensitivity of 0.95, a specificity of 0.88, and an impressive AUC of 0.92. Notably, Dataset 4 yielded the most promising results among all datasets, underscoring the effectiveness of our approach.

Conclusion

Our research substantiates the superiority of our model over previous methodologies and pre-trained models. Furthermore, it significantly contributes to global efforts in combating COVID-19 by providing an advanced diagnostic tool. This work also paves the way for future breakthroughs in the field of medical image analysis.