The outbreak of the COVID-19 pandemic caused the death of a large number of people. Millions ofpeople are infected by this virus and are still getting infected day by day. As the cost and required time ofconventional RT-PCR tests to detect COVID-19, researchers are trying to use medical images like X-Ray andComputed Tomography (CT) images to detect it with the help of Artificial Intelligence (AI) based systems. Inthis paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 frommedical images using X-Ray or CT of lung images. We collected information about available research resourcesand inspected a total of 80 papers from the time period of February 21, 2020 to June 20, 2020. We explored andanalyzed datasets, preprocessing techniques, segmentation, feature extraction, classification and experimentalresults which can be helpful for finding future research directions in the domain of automatic diagnosis ofCovid-19 disease using Artificial Intelligence (AI) based frameworks.

The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.

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Image captioning using encoder-decoder-based approach where CNN is used as the Encoder and sequence generator like RNN as Decoder has proven to be very effective. However, this method has a drawback, that is, sequence needs to be processed in order. To overcome this drawback, some researchers have utilized the transformer model to generate captions from images using English datasets. However, none of them generated captions in Bengali using the transformer model. As a result, we utilized three different Bengali datasets to generate Bengali captions from images using the transformer model. Additionally, we compared the performance of the transformer-based model with a visual attention-based encoder-decoder approach. Finally, we compared the result of the transformer-based model with other models that employed different Bengali image captioning datasets.

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