Abstract
Lung cancer has been listed as one of the world’s leading causes of death. Early diagnosis of lung nodules has great significance for the prevention of lung cancer. Despite major improvements in modern diagnosis and treatment, the five-year survival rate is only 18%. Before diagnosis, the classification of lung nodules is one important step, in particular, because automatic classification may help doctors with a valuable opinion. Although deep learning has shown improvement in the image classifications over traditional approaches, which focus on handcraft features, due to a large number of intra-class variational images and the inter-class similar images due to various imaging modalities, it remains challenging to classify lung nodule. In this paper, a multi-deep model (MD model) is proposed for lung nodule classification as well as to predict the image label class. This model is based on three phases that include multi-scale dilated convolutional blocks (MsDc), dual deep convolutional neural networks (DCNN A/B), and multi-task learning component (MTLc). Initially, the multi-scale features are derived through the MsDc process by using different dilated rates to enlarge the respective area. This technique is applied to a pair of images. Such images are accepted by dual DCNNs, and both models can learn mutually from each other in order to enhance the model accuracy. To further improve the performance of the proposed model, the output from both DCNNs split into two portions. The multi-task learning part is used to evaluate whether the input image pair is in the same group or not and also helps to classify them between benign and malignant. Furthermore, it can provide positive guidance if there is an error. Both the intra-class and inter-class (variation and similarity) of a dataset itself increase the efficiency of single DCNN. The effectiveness of mentioned technique is tested empirically by using the popular Lung Image Consortium Database (LIDC) dataset. The results show that the strategy is highly efficient in the form of sensitivity of 90.67%, specificity 90.80%, and accuracy of 90.73%.
Highlights
Lung cancer is the world's most prevalent and deadly type of cancer
To address the present challenges, in this paper, a multideep model is used for classification of a lung nodule in LIDC datasets, which consists of multi-scale dilated convolutional layer blocks, dual DCNNs, and multi-task learning component
This reveals that each component of designed model, which is VGG-16, ResNet-50 achieves an improvement in the precision of over more than 3% relative to the standard VGG-16 and ResNet-50 norm since integrating the multi-task learning component into a dual-DCNN architecture
Summary
Lung cancer is the world's most prevalent and deadly type of cancer. The failure to diagnose the early stages of lung cancer is one cause of higher mortality induced by lung cancer because symptoms usually appear in the final stages [1]. In [16,17], the pre-trained DCNN architectures have been used due to robust learning capability from large scale datasets like ImageNet to solve the generally small amount of data visual recognition problems. The main issue of classifying lung nodules is inter-class ambiguity and intra-class variations [18], which pose complex challenges in different modalities in the differentiation of benign lesions from malignant ones. To address the present challenges, in this paper, a multideep model is used for classification of a lung nodule in LIDC datasets, which consists of multi-scale dilated convolutional layer blocks, dual DCNNs, and multi-task learning component. The multi-task learning technique is applied on input pair of images where it will classify the multi-scale dilated Convolutional layers (MsDc) paired images which belong to the similar class or not and helps to classify between malignant and benign. It is proved that the proposed model is state-of-the-art on lung nodule classification problem
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