Detection of Lungs Tumors in CT Scan Images Using Convolutional Neural Networks
Amjad Rehman, Majid Harouni, Farzaneh Zogh, Tanzila Saba, Mohsen Karimi, Faten S. Alamri, Gwanggil Jeon
Abstract
Current human being's lifestyle has caused / exacerbated many diseases. One of these diseases is cancer, and among all kinds of cancers like, brain pulmonary; lung cancer is fatal. The cancers could be detected early to save lives using Computer Aided Diagnosis (CAD) systems. CT scans medical images are one the best images in detecting these tumors in lungs that are especially accepted among doctors. However, the location, random shape of tumors, and poor quality of CT scan images are among the main challenges for physicians in identifying these tumors. Therefore, deep learning algorithms have been highly regarded by researchers. This paper proposed a new model for tumors and nodules segmentation in CT scans images based on convolution neural network (CNN) algorithm. The proposed model comprises preprocessing and postprocessing for fine segmentation of nodules. Filtering is used for image enhancement in preprocessing, and morphological operators are used for fine segmentation in post-processing. Finally, the active counter algorithm implementation exhibited tumors and nodules detection precisely. The sensitivity assessment and dice similarity criteria qualitatively measure the proposed model efficiency on the benchmark dataset. The obtained results with 98.33% accuracy 99.25% validity,98.18% dice similarity criterion show superiority of the proposed model.