Application of Deep Learning in Lung Cancer Imaging Diagnosis

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.

Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis

“…DL is an AI algorithm and an advanced machine-learning method that uses neural networks (Figure 10) [6]. Currently, AI techniques, ranging from machine learning (ML) to DL, are prevalent in healthcare for disease diagnosis, drug discovery, drug development, and patient risk identification [101][102][103][104][105][106][107][108][109][110]. Advances in DL have led to significant progress in the field of medical image analysis and understanding [111].…”

Deep fusion of gray level co-occurrence matrices for lung nodule classification

“…The texture features are extracted through GLCM. Jiang et al [52] used a 3D-convolution-LSTM architecture to classify lung nodules where the input CT scans were first fed into a convolution layer, and LSTMs are then deployed to detect the nodule mask. Mhaske et al [53] proposed a CAD system containing CT scan segmentation to detect lung nodules through Otsu thresholding, features extraction by CNN, and classification by RNN-LSTM.…”

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

“…In comparison to other classifers, a CNN requires little preprocessing. Although the flters are hand-engineered in a primitive way, CNN can learn these flters/features through adequate training [332]. Table 12 describes the usage of CNN to detect lung nodules and cancer.…”