You are required to analyse the dataset below.

Classification of X$-$Ray images to identify patients$$ lungs affected by Covid$-19.$ Classify X$-$Ray images into covid$-$infected or normal X$-$Ray images. $($Data source: COVID$-19$ Radiography Dataset $|$ Kaggle$)$

Based on this dataset, build a Machine Learning $/$ Deep Learning model that can classify the data into associated classes.

Your report should contain some background study and methodology followed by results and discussion. Further, a reflection on the efficacy of the technique$($s$)$ used and an appendix with code $($or a link to online repository $/$ Jupyter Notebook$)$ should also be included. This will be equivalent to a $3000-$word report.

Follow the steps to complete this assessment

Setting the context

In this section, you will describe the dataset by explaining its variables, and the business context. You can develop the context relevant to the dataset that you are analyzing. Further, you will briefly describe the analytics that you are focusing on$.$ Apply some data visualization tools to describe the data characteristics such as its distribution over classes $($number of instances belonging to one class$).$ You can use graphs to present the data distribution. It will also help you describe the bias in the data and its reflection in the results. Please note that it is not expected that you will remove bias from data, but reporting it properly is crucial.

Select Machine Learning$/$Deep Learning Model

Based on your data you can now select a machine learning or deep learning model to do the classification task. There should be sufficient justification for this selection. Describe the selected model briefly and explain different parameter values.

Results

This section will present the classification results. You can document some or all of the possible results such as Confusion Matrix, Accuracy, Root Mean Squared Error, Precision, Recall, etc. You can also use graphs to present some of the results.

Discussion

This section will present a critical discussion about the results presented in the previous section. Describe the results in view of data distribution and bias $($if any$).$ Compare your results with at least two other published studies on the same data set using the same or any other machine learning or deep learning algorithm.

The report should be formatted as follows:

a$.$ Abstract $(10\%)$

b$.$ Introduction $(5\%)$

c$.$ Literature review $(5\%)$

d$.$ Methodology $(10\%)$

e$.$ Results $(10\%)$

f$.$ Conclusion $(8\%)$

g$.$ References $(2\%)$