An efficient deep neural network to segment lung nodule using optimized HDCCARUNet model

Abstract

Lung cancer is a severe disease that may lead to death if left undiagnosed and untreated. Lung cancer recognition and segmentation is a difficult task in medical image processing. The study of Computed Tomography (CT) is an important phase for detecting abnormal tissues in the lung. The size of a nodule as well as the fine details of nodule can be varied for various images. Radiologists face a difficult task in diagnosing nodules from multiple images. Deep learning approaches outperform traditional learning algorithms when the data amount is large. One of the most common deep learning architectures is convolutional neural networks. Convolutional Neural Networks use pre-trained models like LeNet, AlexNet, GoogleNet, VGG16, VGG19, Resnet50, and others for learning features. This study proposes an optimized HDCCARUNet (Hybrid Dilated Convolutional Channel Attention Res-UNet) architecture, which combines an improved U-Net with a modified channel attention (MCA) block, and a HDAC (hybrid dilated attention convolutional) layer to accurately and effectively do medical image segmentation for various tasks. The attention mechanism aids in focusing on the desired outcome. The ability to dynamically allot input weights to neurons allows it to focus only on the most important information. In order to gather key details about different object features and infer a finer channel-wise attention, the proposed system uses a modified channel attention (MCA) block. The experiment is conducted on LIDC-IDRI dataset. The noises present in the dataset images are denoised by enhanced DWT filter and the performance is analysed at various noise levels. The proposed method achieves an accuracy rate of 99.58 % . Performance measures like accuracy, sensitivity, specificity, and ROC curves are evaluated and the system significantly outperforms other state-of-the-art systems.