Detection and classification of lung cancer computed tomography images using a novel improved deep belief network with Gabor filters

The computer-aided diagnosis (CAD) method plays a considerable role in the automated recognition of medical images, considering the increasing numbers of lung cancer patients. Pulmonary nodules are a type of lung irregularity that can be detected early in the course of a patient's life using CAD-based pulmonary patient diagnosis. For several classification methods of lung CT images, the existing method has a lower performance in early cancer diagnosis and a longer processing time. To improve the performance of the classification method in terms of processing time and accuracy, we propose a novel method to classify lung CT images. In the proposed method of lung cancer classification, we use Gabor filters with an enhanced Deep Belief Network (E-DBN) with multiple classification methods. In this E-DBN, we use two cascaded RBMs, Gaussian-Bernoulli (GB) and Bernoulli–Bernoulli (BB) RBMs. A support vector machine (SVM) gives the best performance parameters of all applied methods. Our proposed model uses a support vector machine with an E-DBN to classify lung CT images and improve performance parameters like accuracy, sensitivity, specificity, F-1 score, false positive rate (FPR), false negative rate (FNR), and ROC curve. The proposed approaches are tested and evaluated using three publicly available datasets: the LIDC-IDRI and LUNA-16 datasets.