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Covid-19 Detection using Deep Learning by Sneha Priya

The aim of the project is to detect Covid-19 positive or negative using Deep Learning

Tech Stack used:

Python Numpy Pandas Keras Tensorflow CNN

                                                                    Deep Learning

Deep Learning has proved to be a very powerful tool because of its ability to handle large amounts of data. The interest to use hidden layers has surpassed traditional techniques, especially in pattern recognition. One of the most popular deep neural networks is Convolutional Neural Networks.

                                                                        CNN

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. There are two main parts to a CNN architecture:

A convolution tool that separates and identifies the various features of the image for analysis in a process called as Feature Extraction A fully connected layer that utilizes the output from the convolution process and predicts the class of the image based on the features extracted in previous stages.

image

Dataset

Covid+ Positive Samples having 142 samples taken from github , Covid X-Ray Image Dataset - https://github.com/ieee8023/covid-che... for positive cases. Normal Chest Xrays , Kaggle X-Ray Chest Images - https://www.kaggle.com/paultimothymoo... for negative cases.

Future Scope :

Blood Tests, X-ray are Costly and take time to conduct so the spread can be detected using Deep learning models and will save lot of time for patients.

Conclusion

  1. Built Deep learning CNN model.
  2. learnt keras and tensorflow
  3. These concepts can further be extended to develop more models.