A Comparative study of Lung Cancer Detection and Classification approaches in CT images

Lung disease is a genuine medical problem. In India there are roughly 70,275 individual cases in every year are determined to have lung malignancy. However, early identification and treatment can increase the survival rate. Usually Computed Tomography scan imaging is used in the medical field because of its high clarity and low noise. Only CT scans cannot give proper interpretation to radiologist and the medical practitioner, therefore the Computer Aided Diagnosis system will be extremely useful for radiologists to detect the cancer precisely. Many Computer aided system using image processing and Machine learning has been designed. In this survey various segmentation, feature extraction and classification techniques are considered such as Artificial Neural Network, Convolutional Neural Network, SVM, Gray level co-occurrence matrix, Discrete wavelet transform and many more. We observed that the SVM classifier achieved 96% accuracy, ANN achieved 99% accuracy, CNN achieved 94% accuracy and DNN achieved 97% accuracy.