Applications in Real Life & Future Work

The project built in the previous lines cannot be directly applied, however, a lot of such applications can be built on a similar tangent for serving the purpose of preliminary medical diagnosis based on inputs of patients saving a lot of screening stage costs to the medical industry. The machine learning pipeline presented in the project can be taken up a notch by making it dynamic in nature. By adding more training data dynamically to the model and train it on them to improve its accuracy. The ML Model can be converted into a REST API making the application more robust in nature and scalable. A MySQL Database could be used for storing patient data with diagnosis details and other parameters. I’ve presented a use-sketch diagram for illustrating the architecture of the application which could be built in the near future to be applied in the medical industry.

A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. The Deep Learning model was trained on a publicly available dataset, the SARS-COV-2-Ct-Scan Dataset. The purpose of this project is to apply Convolutional Neural Network (CNN) Architectures in solving problems of the pandemic on a preliminary stage.

Tools and Technologies Used

Some important libraries and technologies used are listed below

• Programming Language: Python
• Web Framework: Django
• Machine Learning Framework: Tensorflow
• Frontend Dev: HTML, CSS (BootStrap)
• Essential Libraries: keras, sklearn, venv, seaborn, matplotlib

A detailed list of all the libraries can be found here.