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In today’s medicine, Computer-Aided Diagnosis Systems (CAD) are very used to improve the screening test accuracy of pulmonary nodules. Processing, classification, and detection techniques form the basis of CAD architecture. In this work, we focus on the classification step in a CAD system where we use Discrete Cosine Transform (DCT) along with Convolutional Neural Network (CNN) to perform an efficient classification method for pulmonary nodules. Combining both DCT and CNN, the proposed method provides high-level accuracy that outperforms the conventional CNN model. Keywords—Convolutional neural network; discrete cosine transform; pulmonary nodule classification; computer aided diagnosis systems

Efficient Lung Nodule Classification Method using Convolutional Neural Network and Discrete Cosine Transform