Deep lung nodule detection using multi-resolution analysis on computed tomography images

Abstrak

The lung nodule must be detected early because the patient's outcome can be enhanced following the lung cancer diagnosis. The candidate research proposed a novel computer-aided detection system based on multi-resolution technique (MRT) and local Gaussian distribution (LGD) methods to accurately identify and label the lung nodules in a computed tomography (CT) screening image. The research aimed to find all the potential nodule constructs, which combined wavelet and multiscale morphological analysis and then used the LGD method to calculate the Gaussian function parameters for each image block. Subsequently, we calculated the probability that each pixel belongs to a particular institute, which shall be used to achieve lung nodule segmentation reliably. After the segmentation, the research employed a convolutional neural network (CNN) variant to improve the detection performance further. The proposed method attained an accuracy of 0.9958, a sensitivity of 0.7899, a specificity of 0.9994 and an F1-score of 0.8651. The comparison with other methods shows that the proposed method had better detection accuracy than the different methods in terms of lung nodule detection.