Detection of Lung Nodules in Chest Radiographs using Wiener Filter and D-CNN Model

Abstract

The automatic and accurate detection of lung tumors or nodules plays a vital role in diagnosing lung cancer. Radiologist have to study multiple chest x-rays to diagnose lung cancer. Since it is a very tedious and time-consuming task, lots of automatic nodule detection and classification techniques based on deep learning have been proposed in recent years. As the previous methods completely rely on deep networks, it increases the computational complexity of the nodule detection process, so in this work, we have proposed a modified wiener filter to remove the adaptive noise from the Chest X-Ray (CXR) images which has not been attempted by any researcher, further to make nodules clearer and contrasting we have enhanced the filtered CXR image by using adaptive histogram equalization (AHE). Later, we have performed classification using the simplified VGG network for Deep Convolutional Neural Network (D-CNN) model to detect and classify the lung nodules from CXR images. As a result, we got an accuracy of 92% in training and 94% on validation, which is then compared with the latest state-of-art works to prove the novelty of our proposed method. The dataset used in our study is taken from the Japan Society of Radiological Technology (JSRT). Finally, we have introduced a Computer Interface (CI) through which the proposed work can be implemented in real-time nodule detection applications in the near future.