Novo-NEX: A Tailored Self-Attention Deep Convolutional Neural Network for the Detection of Disparate Morbidity

Abstract

COVID-19 also known as Severe Acute Respiratory Syndrome Corona virus-2 is a contagious disease that is released from tiny droplets containing saliva or mucus from respiratory system of a diseased person who talks, sneeze, or cough. It spreads rapidly through close contact with somebody who is infected or tapping or holding a virus contaminated objects and surfaces. Another infectious illness known as Pneumonia is often caused by infection due to a bacterium in the alveoli of lungs. When an infected tissue of the lungs has inflammation, it builds-up pus in it. To find out if the patient has these diseases, experts conduct physical exams and diagnose their patients through Chest X-ray, ultrasound, or biopsy of lungs. Misdiagnosis, inaccurate treatment, and if the disease is ignored will lead to the patient's loss of life. The progression of Deep Learning contributes to aid in the decision-making process of experts to diagnose patients with these diseases. The study employs a flexible and efficient approach of deep learning applying the Novo-NEX model in predicting and detecting a patient unaffected and affected with the disease employing a chest X-ray image. The study utilized a collected dataset of 7,181 images using a 256 x 256 image resolution with 32 batch size is applied to prove the performance of the Novo-NEX model being trained. The trained-model produced an accuracy rate of 92% during the performance training. Based on the result of testing conducted, the research study can detect and predict COVID-19, bacterial, and viral-pneumonia diseases based on chest X-ray images.