Computer Vision and Radiology for COVID-19 Detection

Abstract

COVID-19 is spreading rapidly throughout the world. As of 14 April 2020, 128,000 people died of COVID-19, while 1.99 million cases in 210 countries and territories were reported in 219.747 cases. As the virus spreads at a very high rate, there is a huge shortage of medical testing kits all over the world. The respiratory system is the part of the human body most affected by the virus, so the use of X-rays of the chest may prove to be a more efficient way than the thermal screening of the human body. In this paper, we are trying to develop a method that uses radiology, i.e. X-rays for detecting the novel coronavirus. Along with the paper, we also release a dataset for the research community and further development extracted from various medical research hospital facilities treating COVID-19 patients.

ModCOVNN: a convolutional neural network approach in COVID-19 prognosis

“…The disorder of the respiratory system can be caused by COVID-19 [9]. The pulmonary system is the portion of the human organism that is the most infected by the virus, and therefore the use of chest X-rays could be a more effective approach to detect whether a patient is affected by COVID-19 or not than thermal scanning [10]. Another challenging aspect of the health care system is that the individuals infected with COVID-19 [11] could have transmitted this disease without any symptoms.…”

Emerging technologies for the management of COVID19: A review

“… [57] Fuzzy Algorithms “Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques” [61] “ShahlaAsadi, MehrbakhshNilashi, Rabab Ali Abumalloh, Sarminah Samad, Ali Ahani, Fahad Ghabban, Salma Yasmin Mohd Yusuf and EkoSupriyanto” [61] “A new emergency response of spherical intelligent fuzzy decision process to diagnose of COVID19” [62] “Shahzaib Ashraf, Saleem Abdullah, and Alaa O. Almagrabi” [62] “A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients” [15] “Kalyan Kumar Jena, Sourav Kumar Bhoi, Mukesh Prasad and Deepak Puthal” [15] TransferLearning Algorithm “Control The COVID-19 Pandemic: Face Mask Detection Using Transfer Learning” [63] “AbdellahOumina, Noureddine El Makhfi and Noureddine El Makhfi” [63] “Computer Vision and Radiology for COVID-19 Detection” [64] “RavneetPunia, Lucky Kumar, Mohd. Mujahid and Rajesh Rohilla” [64] “COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking” [24] “R. Elakkiya, Pandi Vijayakumar and Marimuthu Karuppiah” [24] “Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking” [46] “Ebenezer Jangam, Aaron Antonio Dias Barreto and Chandra Sekhara Rao Annavarapu” [44] “Rapid COVID‑19 diagnosis using ensemble deep transfer learning models from chest radiographic images” [42] “Neha Gianchandani, Aayush Jaiswal, Dilbag Singh, Vijay Kumar and Manjit Kaur” [41] “A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID‑19 Chest X‑ray Dataset” [47] “Nour Eldeen M. Khalifa, FlorentinSmarandache, Gunasekaran Manogaran and Mohamed Loey” [45] “Novel deep transfer learning model for COVID‑19 patient detection using X‑ray chest images” [48] “N.…”

“…Kumar, M. Gupta, D. Gupta and S. Tiwari” [46] “Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning” [65] “Mangena VenuMadhavan, Aditya Khamparia, Deepak Gupta, Sagar Pande, Prayag Tiwari and M. Shamim Hossain” [62] “Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data” [66] “Mukul Sing,Shrey Bansal1, Sakshi Ahuja2, Rahul Kumar Dubey3,Bijaya Ketan Panigrahi2,Nilanjan Dey4” [63] Random Forest, Support Vector Machine (SVM) and KNN “Predicting the Probability of Covid-19 Recovered in South Asian Countries Based on Healthy Diet Pattern Using a Machine Learning Approach” [67] Md. ShowrovHossenand Dip Karmoker [64] “PRELIMINARY DIAGNOSIS OF COVID-19 BASED ON COUGH SOUNDS USING MACHINELEARNING ALGORITHMS” [68] Arup Anupam Deptt. of E&I NIT Silchar, N Jagan Mohan Deptt.…”

A survey of machine learning techniques for detecting and diagnosing COVID‐19 from imaging

“…Resize [4, 14, 16,18,19, 25,30,33,35-38,41,43,45,47,49,55,56,58,60-62,65-68, 70, 73,79-88] Flipping or rotating [14-16, 22, 25, 27, 28, 30,34, 37, 39, 43-47,51, 54,56,60,61,65-68,71,73,77, 83, 86-98 ] Scaling or cropping [4,14,15,16,18,19,24,25,27,35,37,39,43,46,47,51,55,59,60,62,65,71,[83][84][85]92,94,95,97] Contrast adjustment [27,43,66,67,70,76,78,80,81,83,90,91,95,96,99,100] Brightness or intensity adjustment [16,27,43,51...…”