Classification of X-rays images using Deep Convolutional Neural Network: COVID-19
Bore, Laban Kipchirchir
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The increased amount of labeled X-ray image archives has triggered increased research work in the application of statistics, machine learning, deep learning, and computer vision across the different domains. The fresh studies on the application of deep transfer learning (60) CNN to detect and classify few COVID-19 datasets have had major success. COVID-19 dataset has been collected since the outbreak of the COVID-19 viruses in quarter four of 2019. COVID-19 virus confused the diagnosis, treatment, and care of patients because there is no cure and the virus mutates into different fatal variants. This has led to thousands of people dying, increased admission into hospital beds, ICU, and other health facilities. Hundreds of thousands of new infection cases are reported daily across the world. The overburdening of the health system by the COVID-19 virus has caused access to other health services difficult in the under-served world (89). Traditionally, medical doctors carry several tests such as full blood count tests to ascertain if the body is fighting certain pathogens, sputum tests, and chest X-rays. Doctors will examine patients' medical history, carry physical exams such as listening to the lungs with astethoscope for abnormal crackling sounds. The success of this traditional diagnosis process is dependants on the doctors' experience, skills. quality of X-ray images and the availability of patient's historical records. This is almost unattainable and unsustainable in the under-served countries in Africa. The motivation of this paper is to complement the traditional diagnosis and analysis of chest X-ray images by introducing machine classification approaches and state-of-the-art deep residual network ResNet18 (14, 35). According to WHO (58), diagnosis is a process and requires classification steps to inform research, health policies, and care of the patients. An alternative definition is a \pre-existing set of categories agreed upon by the medical profession to designate a specific condition" (43). We applied statistical learning model to separate and classify all the X-Rays images with patchy areas into one distinct class for further research, examination, analysis, and care of the patients. The observed white patchy areas in our X-Rays images was our statistical variables of interest in classifying Chest X-Rays images into COVID-19 and non-COVID-19, pg 3.2. In addition, the final model can be replicated in other non-covid datasets and extended to other related classification tasks. Deep CNN classification model(ResNet18) as a subfield of non-parametric statistics was used for classifying and predicting COVID-19 positive images. The datasets used were COVID-19 positive (184 cases) and the COVID-19 negative cases (5000) were aggregated from different sources. The COVID-19 negative cases was from 10 disease categories (Pneumonia, Pneumothorax, Lung opacity, Fracture, Atelectasis, Edema, pleural, etc). The finetuned deep CNN model (ResNet18) performed significantly with precision (87.5%), sensitivity (75%) and specificity (99.8%). Rerunning the model using larger datasets by adding noise through data augmentation demonstrated sensitivity (90%) and specificity (100%). Hence, when more dataset is fed into the neural model, the classification performance such as precision, AUC and recall improves significantly. This classification model can be used to aid radiologists or medical practitioners in chest X-ray image diagnosis and treatment (59) by categorization, diagnosis, detection, and prediction. Further extension of this research work will focus on using larger COVID-19 or non-COVID-19 datasets with more focus on systematic review around data acquisition, data certification, model development and pitfalls, and explanation construction (39).