A Basic Concept of Image Classification for Covid-19 Patients Using Chest CT Scan and Convolutional Neural Network

Irma Permata Sari; Widodo; Murien Nugraheni; Putra Wanda

doi:10.1109/ICITAMEE50454.2020.9398462

A Basic Concept of Image Classification for Covid-19 Patients Using Chest CT Scan and Convolutional Neural Network

Open

Irma Permata Sari, Widodo, Murien Nugraheni, Putra Wanda

2020 Proceeding - 1st International Conference on Information Technology, Advanced Mechanical and Electrical Engineering, ICITAMEE 2020 Conference paper Cited by 10

Abstract

On March 12, 2020 WHO announced the status of a global pandemic related to the increasing Covid-19 cases. The outbreak has hit around 188 Countries. Healthcare professionals have repeatedly performed laboratory tests to get the right results to patients, such as, check the chest CT images of the patient's lungs. This is an essential role in clinical treatment and teaching task. In this paper, we tried to classify chest CT image of Covid-19 patient. CNN produce spatial characteristic from images so it very expeditious way for image classification problem. Three techniques are evaluated through experiments. The results of the experiments show the test set has 1119 Covid-19 chest CT images and 446 normal chest CT images. The experiment results represent that our offer model delivered the highest accuracy score of 97.57% among the other models, Inception ResNet-V2 and Inception-V3. © 2020 IEEE.

Affiliations

Information Systems and Technology, Universitas Negeri Jakarta, Jakarta, Indonesia; Computer Science, Harbin University of Science Technology, Harbin, Tiongkok, China

View full text (DOI) →

Research at a Glance

Research at a Glance

Topics & SDG Alignment

Collaboration

Author Profile (Selected)

References Overview

Journal & Source

Metadata & Integrity