Lung Cancer Detection using Deep Learning
This project proposes Densent,VGG-like network for detection of lung cancer
Platform : Matlab
Delivery : One Working Day
Support : Online Demo ( 2 Hours)
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Lung Cancer detection using Deep Learning
Timely diagnosis and determination to the type of lung cancer has important clinical significance. Generally, it requires multiple imaging methods to complement each other to obtain a comprehensive diagnosis. In this work, we propose a deep learning model to identify lung cancer type from CT images for patients from hospital . It has a two-fold challenge: artificial intelligent models trained by public datasets cannot meet such practical requires, and the amount of collected patients’ data is quite few. To solve the two-fold problem, we use image rotation, translation and transformation methods to expand and balance our training data, and then densely connected convolutional networks (DenseNet) is used to classify tumor from images collected . Experimental results show that our method can achieve identifying accuracy 89.85%, which performs better than ResNet, VGG16 and AlexNet. This provides an efficient, non-invasive detection tool for pathological diagnosis to lung cancer type.
Lung cancer has become one of the most common causes of death in the world . It is one of the most harmful malignant tumors to human health. Its mortality rate ranks first among malignant tumor deaths and is the number one killer of cancer deaths among men and women worldwide. There are about 1.8 million new cases of lung cancer per year (13% of all tumors), 1.6 million deaths (19.4% of all tumors) in the world , and 5-year survival rate is only 18% . The incidence and mortality of male lung cancer rank first, which is related to the higher smoking rate of men. The incidence of lung cancer in Chinese women ranks second only to breast cancer, and the death rate ranks first. The smoking rate of women is low, but the incidence of lung cancer is still high, which is related to women’s easy access to second-hand smoke, indoor soot, outdoor air pollution and so on. For this reason, more and more experts and scholars have begun to pay attention to the diagnosis and treatment of lung cancer, and the state has also invested more funds to encourage in-depth research on lung cancer, with a view to effective prevention and treatment. Currently, lung cancer can be divided into two types according to the degree of differentiation and morphological characteristics: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). NSCLC includes three subtypes: squamous cell carcinoma, adenocarcinoma and large cell carcinoma . Timely diagnosis of lung cancer type is of great clinical significance to help doctors develop effective treatment programs and improve patients’ survival time and quality of life. Medical image analysis has the supreme advantage in the field of health aspect, especially in the field of non-invasive treatment and clinical examination. CT images are one of the filtering mechanism that use attractive fields to capture images in films .In 1998, LeCun et al. proposed a LeNet-5 neural network model to identify manuscript numbers . In 2012, deep convolutional neural network model AlexNet was proposed and won the award in the ImageNet Large-Scale Visual Recognition Challenge .After that, GoogLeNet was inspired by LeNet, but it has a neoteric inception module . In 2015, ResNet was proposed and enable to train a 152 layers’ network with lower complexity. In 2017, DenseNet was proposed with a completely new connection model that requires less computation and less model complexity. It has made significant improvements over the state-of-the-art CNN on most benchmark tasks and achieved a remarkable success in many applications such as image recognition, electronic transaction fraud events, etc. Although many methods have been proposed for image classification, few of them focus on the classification of lung cancer type from CT images without biopsy. In this work, we use deep learning to identify lung cancer type from CT images of patients It has a two-fold challenge: artificial intelligent models trained by public datasets cannot meet such practical requires, and the amount of collected patients’ data is quite few. To solve the problem, we use densely connected convolutional networks (DenseNet) to classify tumor . Experimental results show that our method can achieve identifying accuracy 89.85%, which performs better than ResNet , VGG16 and AlexNet. This provides an efficient, non-invasive detection tool for pathological diagnosis to lung cancer type.
It is proposed here a DenseNet architecture to classify lung cancer images. DenseNet is a convolutional neural network with dense connectivity, consisting of several dense blocks with dense connectivity and transition layers. In a dense block of L layer introduces L(L + 1)/2 connections, unlike traditional architectures, which only introduces L layer connections. There is a direct connectivity between any two layers. The input to per layer of the network is the union of the outputs of all previous layers, and the feature-maps learned by this layer will be directly transmitted to all subsequent layers as input. a dense block through which feature maps can be concatenated. The lth layer has l inputs, which consists of all the previous feature-maps. Its own feature maps are also passed on to all subsequent layers.
- The proposed system use a deep learning based CNN architecture.
- Where the system improve the accuracy based on learning
- To improve the accuracy in terms of specificity and sensitivity.
In this paper, we designed an automated classification model of lung cancer CT images using DenseNet network . Then denseNet is developed to process the lung cancer datasets and classify the collected data. Experimental results show that our model achieves better classification results in CT image classification of tumors, and the accuracy of the test reached 89.85%. In the future clinical practice, our new method of lung cancer image classification can assist the radiologists’s treatment, simplify the steps of lung cancer diagnosis, improve the accuracy of lung cancer diagnosis, and reduce the rate .In addition, we will use more high-quality lung cancer CT images to process the classification of lung cancer, further improving the accuracy of the network.
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