Abstract:
Lung diseases are the disorders that affect the lungs, the organs that allow us to
breathe and it is the most common medical conditions worldwide especially in India.
The diseases such as pneumonia and normal lung are detected and classified in this
work. The purpose ofthe work is to detect and classify the lung diseases by effective
feature extraction through Convolutional Neural Network. The preprocessing
techniques will remove the noises and the feature extraction are done to extract the
useful features in the image and the feature selection technique will optimize the top
ranking features that are relevant for the tagged image and the classifiers
employed to classify the images and the performance measures are found for the
same and accurate results. Deep learning algorithms, in particular convolutional
networks, have rapidly become a methodology of choice for analysing medical
images. In this project, we will use the dataset called Chest X-Ray Images
(Pneumonia) that can be downloaded from the following link:
https://www.kaggle.com/paultimothvmoonev/chest-xrav-pneumonia/home.
dataset is organized into 3 folders (train, test, Val) and contains subfolders for each
image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2
categories (Pneumonia/Nomial). The project will involve development, training and
testing of a classifier that can classify X-ray images correctly. It is a multi-level
classification problem and a convolutional neural network will be developed for this
purpose.
