A Novel Scalable Machine Learning Model for Attention-based Deep Multiple Instance Learning for COVID-19 Detection using X-ray Images
Keywords:
Corona Virus Disease 2019 (COVID-19), Scalable model, Big Data, Multiple Instance Learning, Attention-based Deep Learning, Machine LearningAbstract
In Wuhan, China, in December 2019, the new coronavirus 2019 (COVID-2019) was found for the first time. This virus quickly spread around the world and turned into a pandemic. It has ruined people's daily lives as well as the health of the public and the budget of the whole world. It is very important to find positive cases as soon as they are found in order to stop the spread of this outbreak and get help to those who are sick as soon as possible. Since there are no reliable automatic toolkits on the market right now, the need for additional diagnostic tools has gone up. During the SARS-CoV-2 pandemic that hit the whole world in 2020, automatic COVID-19 screening with X-rays was of the greatest importance and need. Within the scope of this study, a new model is given for automatically identifying COVID-19 from raw chest X-ray pictures. Because of all the new tools that are being made, every day a large amount of data, often called "Big Data," is made. This information could be very useful in many different areas. When the collection is too big, you can't fit all of the information that needs to be studied into memory at once. In the distributed scalable model for attention-based deep multiple instance learning (SADD-MIL) that we suggest, an X-ray is given a name at the patient level. The X-ray is then seen as a bag of instances. We got X-ray files from many different places that added up to 200 gigabytes. Several research studies have shown that when our method is used on big amounts of data, the results are better overall. In order to make a fair comparison, we made the attention-based deep 3D multiple instance learning (AD3D-MIL) methods scalable and compared it to the SADD-MIL technique that was shown. The Hadoop data format is used by both the planned SADD-MIL and an already existing AD3D-MIL. Based on what we learned from our experiments, the suggested method works much better than the method that is currently used for big X-ray collections.
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