Independent Research: ISysRG Fights COVID-19, For World, For Indonesia

“Faster R-CNNs: Automated Detection of COVID-19 on Chest Computed Tomography (CT) Images”


This research is contributed by the members of the Intelligent System Research Group (ISysRG) Faculty of Computer Science, Universitas Sriwijaya.

Head of ISysRG

Prof. Dr. Ir. Siti Nurmaini M.T

Physician Experts

  • dr. Alexander Edo Tondas
  • Dr. dr. Radiyati Umi Partan
  • dr. Rachmat Hidayat

Research Assistants

  • Muhammad Naufal Rachmatullah, M.T
  • Annisa Darmawahyuni, M.Kom
  • Firdaus, S.T., M.Kom.

External Contributors

  • Wahyu Caesarendra, S.T., M.Eng., Ph.D. (Universiti of Brunei Darussalam)
  • Timotius Wira Yudha (Medical Students of Universitas Sriwijaya)


Ade Iriani Sapitri, Adithia Jovandy, Jannes Effendi, M. El Qiliqsandy, Putri Wulandari, Lia Anggraini, Abdullah Farhan, Ahmad Fansyuri, Ghina Auliya, Bima Kurniawan, Ribowo Agusti Sunoki, Arjuno Gusendi, Pininggit Harun Kusuma Nasution, M. Alfin Sukma Wardani, Ria Esafri, Ryan Darmawan Siregar, Aldi Predyansyah, Xosya Salassa, Rafi Audi Prayoga, Armanda Sanjaya, Liya Anggraini, Alna Yopa Khotimah, Suci Dwi Lestari, Annisa Karima R. Harahap, Irvan Fahreza, Bima Pratama Anom, Tri Agung Hermawan, Tiara Annisa Dina, Nadhya Hassni, M. Divo Trinanda, Leni Estiyani, Rizqi Abraqa Ramadhan, Alif Muhamad Hafidz, Tomi Mandala Putra, Sinta Bella, Azis Mulki Rafani, Muhammad Rizky Aditya Utama, RM. Ardiansyah, M. Adhitya Reski Pratama. R, Irawan, Kms. Irwan Gunawan, Faisal Baja Esa Putra, Ikhsan Nuh Atthalla, Wais Al Qarni.



Coronavirus disease 2019 (COVID-19) outbreak, was firstly reported in Huanan Seafood Wholesale Market, Wuhan, China, which sells live animals. The current research briefly summarized the characteristics of patients who have been infected with COVID-19, MERS-CoV, and SARS-CoV. The incubation period infected by COVID-19 is generally 3 to 7 days and up to 14 days. Fever, fatigue, and dry cough are the main manifestations at the prodromal phase. Other researchers reported a familial cluster of pneumonia associated with the COVID-19, indicating person-to-person transmission. In Indonesia, a positive COVID-19 has 4241 patients confirmed and 373 deaths, 8.79% of contaminated on 12 April 2020 based on Indonesia National Disaster Management Authority.

Currently, high-resolution computed tomography (HRCT) is of outstanding importance as it is the main tool for screening, primary diagnosis, and evaluation of disease severity. Chest CT examination is very important in the initial diagnosis of the new type of pneumonia, and CT changes are variable. By this independent research funding, Intelligent System Research Group (ISysRG) is supported by the COVID-19 pneumonia CT images from Radiopaedia (, case courtesy of Dr. Bahman Rasuli (rID: 74576). The evaluation of CT Images is validated by medician and radiologists. The total number of CT images raw data are 1714; 419 COVID-19 pneumonia, 200 normal lung CT, and 24 non-pneumonic lung disease infected by virus and 1071 by bacteria.

Deep learning, as the outstanding algorithm, can be implemented in medical applications, specifically in image processing for CT. To investigate diagnostic performance by using a deep learning method with a Faster-R Convolutional Neural Networks (Faster R-CNNs) for the object detection of acquired pneumonia and other non-pneumonic lung disease using chest CT.


Sample of CT Images Raw Data

Figure 1. Raw Data Samples

Pre-processing Data

All data around 419 images infected by COVID-19 with pixel size ranging from 256 x 256 to 1500 x 1800. The amount of chest CT images is divided by 80% for the training set and 20% for the testing set. The number of 335 and 84 chest CT images, respectively.

Figure 2. CT Scan Image Preparation
Figure 3. The process of COVID-19 region detection

Faster R-CNNs

This research conducts the architecture that consists of the region proposal network (RPN) as a region algorithm and the Faster R-CNNs as a detector network. The VGG19 and ResNet50 architecture is designed to deliver high area detection accuracy. The Faster R-CNN detector also consists of a backbone of the CNNs, a pooling layer region of interest (ROI) and entirely linked layers followed by two sibling branches for classification and bounding box regression. Backbone modeling with hyperparameters for the VGG16 as RPNs, 50 to 200 epochs, 1 batch size, 0.0001 learning rate, 0.9 momentum, and Stochastic Gradient Descent optimizer for the pre-trained VGG16 model used to learn 2697 from 275 images.

Figure 4. The Faster R-CNNs Architecture for COVID-19 Detection

Progress 1 (Initial Results)

By using CNNs-based object detection approach with Faster-RCNN algorithm, we achieved overall accuracy of 98%. In addition, the high performance of the CNNs-based Faster-RCNN model that we built in this study was checked with 79.3% accuracy using external samples. More specifically, our model achieved relative high sensitivity as a screening tool, 0.88 and 0.83 on the internal and the external dataset, respectively. Besides, the research has compared to the diagnosis of RT-PCR based nucleic acid detection, which recent data have suggested that the accuracy of nucleic acid testing is only about 50-90%. In our initial research using lung CT imaging feature extraction, the research can achieve 98% accuracy. The results outperformed nucleic acid testing.

Figure 5. The result of COVID-19 object detection based on proposed Faster R-CNNs algorithm (Progress 1)

Progress 2 (Final Results)

In the next phase, the pre-tuning and fine-tuning hyperparameters is used for getting the best model. For the object detection of COVID-19 by Faster R-CNNs algorithm, this research achieved 99% accuracy, 98% sensitivity, 100% specificity, and 100% precision on CT chest radiography. The result of CT images can detect two conditions including COVID-19 infected and healthy. Besides, the proposed model with unseen data achieves accuracy, sensitivity, specificity, precion and F1-score are 100%, respectively.

Figure 6. The result of COVID-19 object detection based on proposed Faster R-CNNs algorithm (Progress 2)
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