Classification of malignant lung cancer using deep learning
Vinod Kumar, Brijesh Bakariya
Abstract
In the automatic detection of suspicious shaded regions on CT images derived from the LIDC-IDRI dataset, the diagnostic system plays a significant role. This paper introduces an automatic recognition method for lung nodules of the regions of concern (ROI). The lung regions are segmented from DICOM image size 512 × 512 by adding a median filter, Gaussian filter, Gabor filter and watershed algorithm. AlexNet uses 227 × 227 × 3 with "fc7" (fully connected) layers and GoogLeNet uses 224 × 224 × 3 with "pool5-drop 7 × 7 s1" layers. Here, the authors explain what is better about AlexNet and GoogLeNet through its performance analysis, feature extraction, classification, sensitivity, specificity, detection and false alarm rate with time complexity. A multi-class SVM classifier with 100% precision and specificity provided the best performance in deep learning neural networks.