Deep Learning techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages,
where the accuracy factor is very important to discover the abnormality issues in target images, especially in various cancer tumors such as lung
cancer etc. proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually
preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into
image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch,
respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate
final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum
pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their
performances are evaluated by eightfold crossvalidation. Following the segmentation principles, an enhanced region of the object of interest
that is used as a basic foundation of feature extraction is obtained.
Keywords: — Lung disease Detection, Convolutional Neural Network, Deep Learning, Medical Images.