Description

To develop a screening tool for the detection of interstitial lung disease (ILD) patterns using a deep-learning method. A fully convolutional network was used for semantic segmentation of several ILD patterns. Improved segmentation of ILD patterns was achieved using multi-scale feature extraction. Dilated convolution was used to maintain the resolution of feature maps and to enlarge the receptive field. This project introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises Sparse Stack Auto Encoder and Decoder with Convolutional Neural Network and vgg16 and resnet50 activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers.