ABSTRACT
Thorax diseases are most diagnosed through medical images and are manual and time-consuming. The recent COVID-19 pandemic has demonstrated that machine learning systems can be an excellent option for classifying these medical images. However, a confidence classification in this context is the need. During COVID-19, we first need to detect and isolate COVID-19 patients. When it comes to diagnosing and preventing thoracic disorders, nothing beats the convenience and low cost of a chest X-ray. According to expert opinion on screening chest X-rays, abnormalities were most commonly found in the lungs and hearts. However, in fact, acquiring region-level annotation is costly, and model training mostly depends on image-level class labels in a poorly supervised way, making computer-aided chest X-ray filtering a formidable obstacle. Hence, in this work, we propose a binary, multi-class, and multi-level classification model based on transfer learning models ResNet-50, InceptionNet, and VGG-19. After that, a multi-class classifier is used to know which class it mostly be- longs to. Finally, the multi-level classifier is used to know how many diseases the patient suffers from. This research presents a Binary Multi Class and Multi Level Classification with Dual Priority Labelling (BMCMLC-DPL) model for COVID-19 and other thorax disease detection. Using state-of-the-art deep neural networks (ResNet-50), we have shown how accurate the classification of COVID-19, along with 14 other chest diseases, can be performed. Our classification technique thus achieved an average training accuracy of 98.6% and a test accuracy of 96.52% for the first level of binary classification. For the second level of 16 class classification, our technique achieved a maximum training accuracy of 91.22% and test accuracy of 86.634% by using ResNet-50. However, due to the lack of multi-level COVID-19 patient data, multi-level classification is performed only on 14 classes, showing the state-of-the-art accuracy of the system. © 2022 Lavoisier. All rights reserved.
ABSTRACT
One of the deadliest pandemics is COVID-19, which is cur-rently detected using screening tests like the Reverse Transcription Poly-merase Chain Reaction (RT-PCR) test, requiring ample time. From the study, it is being found that one of the severe side-effects of COVID-19 is COVID-19 Pneumonia. This Pneumonia can be detected using pa-tient’s chest X-ray or Computed Tomology scan (CTscan). Hence, uti-lizing these test images and with the help of machine learning, fast and efficient identification of the COVID-19 patient is possible, which can fur-ther help a vast population. In this paper, a U-Net incorporated convolu-tional neural network (CNN) based approach (CUcovid) is proposed for predicting the presence of COVID-19 abnormalities in a patient’s chest X-ray image. We have trained, and tested the model using a randomly selected parallel running image (X-ray image) and caption (COVID-19, non-COVID-19, and viral/bacterial pneumonia) pairs. First images are pre-processed to generate quantitative data and data augmentation is performed to generated qualitative data. These images are then seg-mented using U-Net architecture. Finally, the CNN-based model is used for image classification and COVID-19 patient detection. The tested re-sult shows that the proposed model has achieved approximtely 99.33% and 90.48 % accuracy in two-class and three-class classification.