Automatic Detection Of Pulmonary Nodules Research Articles

S-Net: an S-shaped network for nodule detection in 3D CT images

Objective. Accurate and automatic detection of pulmonary nodules is critical for early lung cancer diagnosis, and promising progress has been achieved in developing effective deep models for nodule detection. However, most existing nodule detection methods merely focus on integrating elaborately designed feature extraction modules into the backbone of the detection network to extract rich nodule features while ignore disadvantages of the structure of detection network itself. This study aims to address these disadvantages and develop a deep learning-based algorithm for pulmonary nodule detection to improve the accuracy of early lung cancer diagnosis. Approach. In this paper, an S-shaped network called S-Net is developed with the U-shaped network as backbone, where an information fusion branch is used to propagate lower-level details and positional information critical for nodule detection to higher-level feature maps, head shared scale adaptive detection strategy is utilized to capture information from different scales for better detecting nodules with different shapes and sizes and the feature decoupling detection head is used to allow the classification and regression branches to focus on the information required for their respective tasks. A hybrid loss function is utilized to fully exploit the interplay between the classification and regression branches. Main results. The proposed S-Net network with ResSENet and other three U-shaped backbones from SANet, OSAF-YOLOv3 and MSANet (R+SC+ECA) models achieve average CPM scores of 0.914, 0.915, 0.917 and 0.923 on the LUNA16 dataset, which are significantly higher than those achieved with other existing state-of-the-art models. Significance. The experimental results demonstrate that our proposed method effectively improves nodule detection performance, which implies potential applications of the proposed method in clinical practice.

Automatic pulmonary nodule detection on computed tomography images using novel deep learning

Automatic pulmonary nodule detection on computed tomography images using novel deep learning

Performance of artificial intelligence-based software for the automatic detection of lung lesions on chest radiographs of patients with suspected lung cancer

PurposeThis study aimed to evaluate the performance of the commercially available artificial intelligence-based software CXR-AID for the automatic detection of pulmonary nodules on the chest radiographs of patients suspected of having lung cancer.Materials and methodsThis retrospective study included 399 patients with clinically suspected lung cancer who underwent CT and chest radiography within 1 month between June 2020 and May 2022. The candidate areas on chest radiographs identified by CXR-AID were categorized into target (properly detected areas) and non-target (improperly detected areas) areas. The non-target areas were further divided into non-target normal areas (false positives for normal structures) and non-target abnormal areas. The visibility score, characteristics and location of the nodules, presence of overlapping structures, and background lung score and presence of pulmonary disease were manually evaluated and compared between the nodules detected or undetected by CXR-AID. The probability indices calculated by CXR-AID were compared between the target and non-target areas.ResultsAmong the 450 nodules detected in 399 patients, 331 nodules detected in 313 patients were visible on chest radiographs during manual evaluation. CXR-AID detected 264 of these 331 nodules with a sensitivity of 0.80. The detection sensitivity increased significantly with the visibility score. No significant correlation was observed between the background lung score and sensitivity. The non-target area per image was 0.85, and the probability index of the non-target area was lower than that of the target area. The non-target normal area per image was 0.24. Larger and more solid nodules exhibited higher sensitivities, while nodules with overlapping structures demonstrated lower detection sensitivities.ConclusionThe nodule detection sensitivity of CXR-AID on chest radiographs was 0.80, and the non-target and non-target normal areas per image were 0.85 and 0.24, respectively. Larger, solid nodules without overlapping structures were detected more readily by CXR-AID.

DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection.

With the advancement of artificial intelligence, CNNs have been successfully introduced into the discipline of medical data analyzing. Clinically, automatic pulmonary nodules detection remains an intractable issue since those nodules existing in the lung parenchyma or on the chest wall are tough to be visually distinguished from shadows, background noises, blood vessels, and bones. Thus, when making medical diagnosis, clinical doctors need to first pay attention to the intensity cue and contour characteristic of pulmonary nodules, so as to locate the specific spatial locations of nodules. To automate the detection process, we propose an efficient architecture of multi-task and dual-branch 3D convolution neural networks, called DBPNDNet, for automatic pulmonary nodule detection and segmentation. Among the dual-branch structure, one branch is designed for candidate region extraction of pulmonary nodule detection, while the other incorporated branch is exploited for lesion region semantic segmentation of pulmonary nodules. In addition, we develop a 3D attention weighted feature fusion module according to the doctor's diagnosis perspective, so that the captured information obtained by the designed segmentation branch can further promote the effect of the adopted detection branch mutually. The experiment has been implemented and assessed on the commonly used dataset for medical image analysis to evaluate our designed framework. On average, our framework achieved a sensitivity of 91.33% false positives per CT scan and reached 97.14% sensitivity with 8 FPs per scan. The results of the experiments indicate that our framework outperforms other mainstream approaches.

Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets

Background: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering.Methods: The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order.Findings: The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided.Discussion and conclusion: Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.

Performance study of automatic detection of pulmonary nodules based on convolutional neural network

In the medical field, the continuous monitoring of the probability of lung cancer incidence can be known that the actual growth rate is becoming faster and faster, and the complications become more and more serious. According to the research and analysis of experts in various fields, it is found that haze is one of the main factors leading to the occurrence of lung cancer in human body, especially in the urban environment. How to use the theory of artificial intelligence technology to design the automatic detection system of lung nodules is the core issue discussed in the field of medical science and technology at present. On the basis of understanding the status quo of convolutional neural network and lung cancer diagnosis and treatment, this study uses CAD system to complete the detection work from four steps, and combines practical cases to judge the detection performance of this technology, in order to lay a foundation for the development of technical innovation in the medical field.

An improved faster R-CNN algorithm for assisted detection of lung nodules

The morbidity and mortality of lung cancer are increasing rapidly in every country in the world, and pulmonary nodules are the main symptoms of lung cancer in the early stage. If we can diagnose pulmonary nodules in time at the early stage and follow up and treat suspicious patients, we can effectively reduce the incidence of lung cancer. CT (Computed Tomography) has been applied to the screening of many diseases because of its high resolution. Pulmonary nodules show white round shadows in CT images. With the popularity of CT equipment, doctors need to review a large number of imaging results every day. Doctors will misjudge and miss the lesions because of reviewing CT scanning results for a long time. At this time, the method of automatic detection of pulmonary nodules by computer can relieve the pressure of doctors in reviewing CT scan results.Traditional lung nodule detection methods, such as gray threshold method and region growing method, divide the detection process into two steps: extracting candidate regions and eliminating false regions. In addition, the traditional detection method can only operate on a single image, which leads to the inability of this method to detect the batch scanning results in real time. With the continuous development of computer equipment performance and artificial intelligence, the relationship between medical image processing and deep learning is getting closer and closer. In deep learning, object detection methods such as Faster R-CNN、YOLO can complete parallel detection of batch images, and deep structure can fully extract the features of input images. Compared with traditional lung nodule detection methods, it has the characteristics of high efficiency and high precision. Faster R-CNN is a classical and high-precision two-stage object detection method. In this paper, an improved Faster R-CNN model is proposed. On the basis of Faster R-CNN, multi-scale training strategy is used to fully mine the features of different scale spaces and perform path augmentation on lower-dimensional features, which improves the small object detection ability of the model. Through Online Hard Example Mining (OHEM), the loss value is used to quantify the difficulty of candidate region detection, and the training times of the region to be detected are adaptively adjusted. Make full use of prior information to customize the size and proportion of preset boundary anchor boxes. Using deformable convolution to improve the visual field to enhance the global features and enhance the ability to extract the feature information of pulmonary nodules in the same scale space.The new model was tested on LUNA16 (Lung Nodule Analysis 2016) dataset. The detection precision of the improved Faster R-CNN model for pulmonary nodules increased from 76.4% to 90.7%, and the recall rate increased from 40.1% to 56.8% Compared with the mainstream object detection algorithms YOLOv3 and Cascade R-CNN, the improved model is superior to the above models in every index.

Pulmonary nodule detection based on IR-UNet + + .

Lung cancer is one of the cancers with the highest incidence rate and death rate worldwide. An initial lesion of the lung appears as nodules in the lungs on CT images, and early and timely diagnosis can greatly improve the survival rate. Automatic detection of lung nodules can greatly improve work efficiency and accuracy rate. However, owing to the three-dimensional complex structure of lung CT data and the variation in shapes and appearances of lung nodules, high-precision detection of pulmonary nodules remains challenging. To address the problem, a new 3D framework IR-UNet + + is proposed for automatic pulmonary nodule detection in this paper. First, the Inception Net and ResNet are combined as the building blocks. Second, the squeeze-and-excitation structure is introduced into building blocks for better feature extraction. Finally, two short skip pathways are redesigned based on the U-shaped network. To verify the effectiveness of our algorithm, systematic experiments are conducted on the LUNA16 dataset. Experimental results show that the proposed network performs better than several existing lung nodule detection methods with the sensitivity of 1 FP/scan, 4 FPs/scan, and 8 FPs/scan being 90.13%, 94.77%, and 95.78%, respectively. Therefore, it comes to the conclusion that our proposed model has achieved superior performance for lung nodule detection.

A Cascaded Multi-Stage Framework for Automatic Detection and Segmentation of Pulmonary Nodules in Developing Countries.

Lung cancer has the highest mortality rate among all malignancies. Non-micro pulmonary nodules are the primary manifestation of early-stage lung cancer. If patients can be detected with nodules in the early stage and receive timely treatment, their survival rate can be improved. Due to the large number of patients and limited medical resources, doctors take a longer time to make a diagnosis, which reduces efficiency and accuracy. Besides, there are no suitable approaches for developing countries. Therefore, we propose a 2.5D-based cascaded multi-stage framework for automatic detection and segmentation (DS-CMSF) of pulmonary nodules. The first three stages of the framework are used to discover lesions, and the latter stage is used to segment them. The first locating stage introduces the classical 2D-based Yolov5 model to locate the nodules roughly on axial slices. The second aggregation stage proposes a candidate nodule selection (CNS) algorithm to locate further and reduce redundant candidate nodules. The third classification stage uses a multi-size 3D-based fusion model to accommodate nodules of varying sizes and shapes for false-positive reducing. The last segmentation stage introduces multi-scale and attention modules into 3D-based UNet autoencoder to segment the nodular regions finely. Our proposed framework achieves 95.95% sensitivity and 89.50% CPM for nodules detection on the LUNA16 dataset, and 86.75% DSC for nodules segmentation on the LIDC-IDRI dataset. Moreover, our approach also achieves the accuracy-complexity trade-off, which can effectively realize the auxiliary diagnosis of pulmonary nodules in developing countries.

MA04.07 A Controlled Study of Pathological T- staging and Imaging T-staging of NSCLC Based on Artificial Intelligence

The incidence of non-small cell lung cancer (NSCLC) currently ranks first in lung cancer, and accurate staging is the key to its prognosis. Multi-slice computed tomography (MSCT) is the preferred method for evaluating lung cancer staging, prognosis and efficacy. By measuring the maximum diameter of the tumor, more accurate preoperative imaging and clinical T staging can be made. In recent years, the rapid development of artificial intelligence (AI) has brought a lot of convenience to physicians in the automatic detection, measurement and risk assessment of pulmonary nodules.

An attentive and adaptive 3D CNN for automatic pulmonary nodule detection in CT image

Automatic pulmonary nodule detection is the most crucial technology in early diagnosis of lung cancer and early treatment. However, the various nodule type, shape and size, especially the diameter of lung nodules (ranging from 3 mm to 30 mm), make the lung nodule detection results with high false positives. It significantly affects the detection performance of lung nodules. In this paper, an adaptive and attentive 3D Convolutional Neural Network (CNN) is proposed for automatic pulmonary nodule detection, which contains two parts: the candidate nodule detection and false positive reduction. In the first stage, the globule, spital and fine-grained information of focal nodules are scratched by the high-resolution fused attention module in the proposed method. In the second stage, an adaptive 3D CNN structure is designed to further reduce the false positives, which extracts the multilevel contextual information via an adaptive 3D convolution kernel. Extensive experiments are conducted on publicly available LUNA16. The results demonstrate that the proposed method can increase the sensitivity and decrease the false positives rate for automated pulmonary nodule detection effectively.

Automatic Detection and Localization of Pulmonary Nodules in CT Images Based on YOLOv5

<p>Lung cancer has always threatening human health and life. As small pulmonary nodules are main early features of lung cancer, early screening for small pulmonary nodules through computed tomography (CT) imaging is essential for the treatment of lung cancer. In this paper, the YOLOv5 model is improved to improve the ability of detection and recognition of small pulmonary nodules in complex CT lung images. Firstly, the preprocessing step is put into effect to obtain the lung parenchyma in CT images. Then, the backbone structure of YOLOv5 is improved by iResNet to improve the ability of feature extraction, and the feature fusion network is improved by BiFPN to improve the detection ability of small pulmonary nodules. Finally, the strategy of group normalization is used to improve the model performance under small batch size training condition. The experimental results on LUNA16 data set show that the detection AP of the improved model reach 94.8%, the competitive index score is 0.895, and the sensitivity is 78.1%, 94.4%, under 1/8 and 1/4 FPs, respectively. Compared with other two-dimensional target detection models, the improved yolov5 model has better detection ability of small pulmonary nodules. And, the results are better than most other two-dimensional pulmonary nodule detection methods. In addition, compared with other three-dimensional pulmonary nodule detection methods.</p> <p> </p>

Evaluation of Automatic Detection of Pulmonary Nodules and Pneumonia from Chest Radiographs Using Deep-learning-based Algorithm

Evaluation of Automatic Detection of Pulmonary Nodules and Pneumonia from Chest Radiographs Using Deep-learning-based Algorithm

Fast fully automatic detection, classification and 3D reconstruction of pulmonary nodules in CT images by local image feature analysis

This study develops a computer-aided diagnosis (CAD) system for automatic detection and classification of pulmonary nodules in lung computed tomography (CT) images, which can simultaneously detect and classify ground-glass opacity (GGO), part-solid, and solid nodules. From the morphological feature and local image features of pulmonary nodules, a total of ten features were selected for artificial neural network (ANN) training and testing, to ensure the system can detect and classify pulmonary nodules. Then, the cross-sectional area of each slice of pulmonary nodule was extracted. The response evaluation criteria in solid tumor (RECIST) value was obtained using the Euclidean distance formula, and the number of pixels of the maximum cross-sectional area was counted for area computation. The nodule volume and 3D reconstruction was obtained using the marching cube algorithm and Riemann integral formula. The sensitivity of system detection and classification were 93.13% and 92.70%, respectively. The proposed system can detect GGO, part-solid, and solid nodules, and takes only 0.1 s to process a single image. This study used the clay model imitating pulmonary nodule morphology as physical samples. Each physical sample underwent three CT. The average difference between the physical volume and the volume derived from this study was 0.37%. The detection and classification results of the system enhanced the clinical detection of missing nodules. 3D reconstruction and volume information of the nodules can give their volume doubling time and growth rate when the patient undergoes CT for the second time, thus enhancing the effect of diagnosis and treatment.

Automatic detection of solitary pulmonary nodules using superpixels segmentation based iterative clustering approach

Lung disease is the premier cause of cancer deaths all over the world. The lung cancer diagnosis is mediocre at an early stage since it is impotent by the radiologist. Various investigations conducted so far manifest clearly that the nodule segmentation algorithms are inefficient. Thus, this investigation has centralized superpixels segmentation based iterative clustering (CSSBIC) and a sophisticated optimization approach for detailed segmentation of pulmonary nodules. The supreme intent of this research paper is the enhancement of lung CT images to identify the tumor effectively and small-scale anomalous nodules segmentation in the lung area. The initial phase an-isotropic diffusion with masking (AIDME) enhancement techniques which can eliminate the noise discern in the images. The next step is to apply the centralized superpixels segmentation based iterative clustering (CSSBIC) algorithm by using an improved picture sequence nodule for abnormal lung tissue prediction. The lung nodule is essentially retrieved using a GWO, based on deep learning techniques, with advanced ONN (ONN) and advanced CNN (CNN). The average time for segmenting the Nodule Slice order is 1.06 s. The best classification accuracy is 97% via GWO based advanced one nearest neighbour (AONN) and 97.6% by GWO based advanced convolutional neural network (ACNN) classifier.

P42.02 Evaluating the Feasibility of a Deep Learning-Based Computer-Aided Detection System for Lung Nodule Detection in a Lung Cancer Screening Program

Deep learning techniques have recently achieved remarkable results in automated lung nodule detection. Previously, we developed a deep learning-based computer-aided detection (DL-CAD) system based on the CT scans from the United States. The purpose of this study was to validate its effectiveness for automatic pulmonary nodule detection in an external validation set from a lung cancer screening program.

肺结节图像的计算机自动检测研究进展

肺结节图像的计算机自动检测研究进展

Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data.

To develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation. The developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You Only Look Once (YOLO) v3, as a deep learning approach. This method is featured by two distinct properties: (I) an automatic multi-scale feature extractor for nodule feature screening, and (II) a feature-based bounding box generator for nodule localization and diameter estimation. Two independent studies were performed to train and evaluate this CAD method. One study comprised of a computer simulation that utilized computer-based ground truth. In this study, 300 CT scans were simulated by Cardiac-torso (XCAT) digital phantom. Spherical nodules of various sizes (i.e., 3-10 mm in diameter) were randomly implanted within the lung region of the simulated images-the second study utilized human-based ground truth in patients. The CAD method was developed by CT scans sourced from the LIDC-IDRI database. CT scans with slice thickness above 2.5 mm were excluded, leaving 888 CT images for analysis. A 10-fold cross-validation procedure was implemented in both studies to evaluate network hyper-parameterization and generalization. The overall accuracy of the CAD method was evaluated by the detection sensitivities, in response to average false positives (FPs) per image. In the patient study, the detection accuracy was further compared against 9 recently published CAD studies using free-receiver response operating characteristic (FROC) curve analysis. Localization and diameter estimation accuracies were quantified by the mean and standard error between the predicted value and ground truth. The average results among the 10 cross-validation folds in both studies demonstrated the CAD method achieved high detection accuracy. The sensitivity was 99.3% (FPs =1), and improved to 100% (FPs =4) in the simulation study. The corresponding sensitivities were 90.0% and 95.4% in the patient study, displaying superiority over several conventional and CNN-based lung nodule CAD methods in the FROC curve analysis. Nodule localization and diameter estimation errors were less than 1 mm in both studies. The developed CAD method achieved high computational efficiency: it yields nodule-specific quantitative values (i.e., number, existence confidence, central coordinates, and diameter) within 0.1 s for 2D CT slice inputs. The reported results suggest that the developed lung pulmonary nodule CAD method possesses high accuracies of nodule localization and diameter estimation. The high computational efficiency enables its potential clinical application in the future.

Detection of pulmonary nodules based on a multiscale feature 3D U-Net convolutional neural network of transfer learning.

A new computer-aided detection scheme is proposed, the 3D U-Net convolutional neural network, based on multiscale features of transfer learning to automatically detect pulmonary nodules from the thoracic region containing background and noise. The test results can be used as reference information for doctors to assist in the detection of early lung cancer. The proposed scheme is composed of three major steps: First, the pulmonary parenchyma area is segmented by various methods. Then, the 3D U-Net convolutional neural network model with a multiscale feature structure is built. The network model structure is subsequently fine-tuned by the transfer learning method based on weight, and the optimal parameters are selected in the network model. Finally, datasets are extracted to train the fine-tuned 3D U-Net network model to detect pulmonary nodules. The five-fold cross-validation method is used to obtain the experimental results for the LUNA16 and TIANCHI17 datasets. The experimental results show that the scheme not only has obvious advantages in the detection of medium and large-sized nodules but also has an accuracy rate of more than 70% for the detection of small-sized nodules. The scheme provides automatic and accurate detection of pulmonary nodules that reduces the overfitting rate and training time and improves the efficiency of the algorithm. It can assist doctors in the diagnosis of lung cancer and can be extended to other medical image detection and recognition fields.

Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.