Abstract
Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.
Highlights
With the deterioration of the environment caused by more serious air pollution and factors such as smoking and occupational exposure, the number of lung cancer patients worldwide has increased and the incidence rate has increased year by year. ere are approximately 1.4 million lung cancer cases worldwide each year, and nearly 60% of them will be examined
Efficiency, and ease of learning of deep learning technology, its application in the field of medical treatment has become a popular direction. erefore, in the medical application prospects of this study, we have developed an intelligent medical detection algorithm to assist doctors in the detection of known organs and diseases, which greatly improves the efficiency of doctors’ diagnoses
For images in “mhd” format, data in “mhd” format includes two parts, one is the raw file storing the content of the entire data, and the other is the “mhd” file storing the relevant information of the entire Computed Tomography (CT) image, such as offset value. e file in “mhd” format can be read through the toolkit “SimpleITK.” After the reading is completed, the position coordinates need to be corrected by the offset value recorded in the header file and converted from world coordinates to their own coordinates. e subsequent operation procedures for the images of these two types of formats are the same as shown in the subsequent steps (ii) Due to the particularity of CT images, the pixel distances of each axis are different
Summary
With the deterioration of the environment caused by more serious air pollution and factors such as smoking and occupational exposure, the number of lung cancer patients worldwide has increased and the incidence rate has increased year by year. ere are approximately 1.4 million lung cancer cases worldwide each year, and nearly 60% of them will be examined. Rough medical image, imaging processing technology, combined with computer analysis capabilities, greatly improves the efficiency and accuracy of doctors’ diagnosis of film reading. With the continuous deepening of related research work, in recent years, deep learning technology as an upgraded version of neural networks has gradually solved many practical problems in the fields of pattern recognition, speech processing, biomedicine, economic forecasting, etc., and has shown outstanding performance. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) as part of deep learning technology are used in speech and text processing in medical diagnosis [3, 4]. Erefore, in the medical application prospects of this study, we have developed an intelligent medical detection algorithm to assist doctors in the detection of known organs and diseases, which greatly improves the efficiency of doctors’ diagnoses.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
