# Classification of COVID-19 via Homology of CT-SCAN

In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic. There are more than 110 Million infected cases on the globe, sofar. Due to its promptness and effective results computed tomography (CT)-scan image is preferred to the reverse-transcription polymerase chain reaction (RT-PCR). Early detection and isolation of the patient is the only possible way of controlling the spread of the disease. Automated analysis of CT-Scans can provide enormous support in this process. In this article, We propose a novel approach to detect SARS-CoV-2 using CT-scan images. Our method is based on a very intuitive and natural idea of analyzing shapes, an attempt to mimic a professional medic. We mainly trace SARS-CoV-2 features by quantifying their topological properties. We primarily use a tool called persistent homology, from Topological Data Analysis (TDA), to compute these topological properties. We train and test our model on the "SARS-CoV-2 CT-scan dataset" \citep{soares2020sars}, an open-source dataset, containing 2,481 CT-scans of normal and COVID-19 patients. Our model yielded an overall benchmark F1 score of $99.42\%$, accuracy $99.416\%$, precision $99.41\%$, and recall $99.42\%$. The TDA techniques have great potential that can be utilized for efficient and prompt detection of COVID-19. The immense potential of TDA may be exploited in clinics for rapid and safe detection of COVID-19 globally, in particular in the low and middle-income countries where RT-PCR labs and/or kits are in a serious crisis.

PDF Abstract

## Code Add Remove Mark official

No code implementations yet. Submit your code now

## Datasets

Add Datasets introduced or used in this paper

## Results from the Paper Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.