An End to End Hybrid Learning Model for Covid-19 Detection from Chest X-ray Images

Abstract

Covid-19 was a global phenomenon which spread rapidly and cost so many lives across the globe. It can be detected at early stages from radiology scans using Deep Learning. This Research analyses the comparison between a Hybrid Learning Model and pre-trained models VGG19, Xception and MobileNet. The aim of the research was to classify the Chest X-Ray scans as COVID-19 positive or negative using deep learning techniques. The results showed that the Hybrid Learning model built from scratch produced better accuracy than other transfer learning approaches. These results show us that implementing these Computer-aided diagnoses (CAD) systems in hospitals and clinics can be an efficient way of detecting COVID-19 presence from chest X-rays. This method can provide much more accurate results and timely diagnosis and cure for patients.

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