Automatic Detection Of Lung Nodules Research Articles

Automatic detection of lung nodules in computed tomography images using U-Net

Automatic detection of lung nodules in computed tomography images using U-Net

Automatic Lung Nodules Detection Using a Modified YOLOv5

Lung cancer diagnosis involves a detection strategy to determine the specific location of the abnormality and its likelihood whether cancerous or non-cancerous. In existing methods such as Endobronchial ultrasound-guided transbronchial needle extraction (EBUS-TBNA) method requires specific equipment and trained operators. The limits in biomarker discovery begin with sample collection, transportation, representative tissue processing, reference standards, assay sensitivity and specificity. Liquid biopsy method requires tissue biopsy for initial diagnosis and may increase the likelihood of false negatives and false positives. To overcome these challenges, initially in this paper, three detectors are trained for nodule detection i.e. You Only Look Once version 3 (YOLOv3), YOLOv4 and YOLOv5. YOLOv3 achieved precision of 72%, recall of 75%, mean Average Precision (mAP) of 70%, F1 Score of 0.73 and Giga Floating-Point Operations (GFLOPs) of 30. In contrast, YOLOv4 achieved 85% precision, 70% recall, 80% mAP, F1 score of 0.76 and 65 GFLOPs. On the other hand, YOLOv5 achieved precision of 90%, recall of 80%, mAP of 85%, F1 Score of 0.85 and FLOPs of 217. These three detectors also faced few challenges like complexity, have high computation time and low performance. So, to overcome the problems of YOLO based methods, a modified YOLOv5 model has been proposed for the automatic detection of lung nodules in CT scans. Key modifications include enhanced feature extraction layers and customized anchor boxes tailored for small nodule detection. These modifications demonstrate the model's potential for reliable and efficient lung cancer screening, aligning with the manuscript's focus on advancing detection techniques through customized YOLOv5 enhancements. The modified model achieves a 90% precision, 85% recall, 88% mAP, 0.87 F1 score and 35.2 GFLOPs. These results represent an improvement in accuracy and increase in sensitivity compared to the standard YOLOv5 model. Also, GFLOPs have been reduced which demonstrates low computing requirement for the proposed model. The proposed model could be further used as clinical tool for lung cancer diagnosis.

Retraction Note: Optimized convolutional neural network for automatic lung nodule detection with a new active contour segmentation

Retraction Note: Optimized convolutional neural network for automatic lung nodule detection with a new active contour segmentation

Validation of a commercially available CAD-system for lung nodule detection and characterization using CT-scans.

This study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans. This retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1's detection sensitivity was also assessed. A total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans), of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD's detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2%), 42/47 (89.4%), and 25/42 (59.5%), respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8%), 16/42 (38.1%), and 18/25 (72.0%), respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9%), 123/149 (82.6%), and 141/149 (94.6%) for calcification, spiculation, and location, respectively. Although the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader's detection rate. While promising characterization performances are obtained, the tool's performance in terms of texture classification remains a subject of concern. Numerous lung nodule computer-aided detection-systems are commercially available, with some of them solely being externally validated based on their detection performance on solid nodules. We encourage researchers to assess performances by incorporating all relevant characteristics, including part-solid and ground-glass nodules. Few computer-aided detection (CAD) systems are externally validated for automatic detection and characterization of lung nodules. A detection sensitivity of 81.4% and an overall texture classification sensitivity of 77.2% were measured utilizing CAD. CAD has the potential to increase single reader detection rate, however, improvement in texture classification is required.

Pulmonary Nodule Segmentation Network Based on Res Select Kernel Contextual U-Net

Abstract U-Net network is widely used in the field of medical image segmentation. The automatic segmentation and detection of lung nodules can help in the early detection of lung cancer. Therefore, in this paper, to solve the problems of small proportion of nodules in computer tomography (CT) images, complex features, and insufficient segmentation accuracy, an improved U-Net network based on residual network and attention mechanism was proposed. The feature extraction part of Res select Kernel Contextual U-Net (RkcU-Net) network is based on Res2net, a variant of Resnet, and on which a feature extraction module with automatic selection of convolution kernel size is designed to perform multiscale convolution inside the feature layer to form perceptual fields of different sizes. This module selects the appropriate convolution kernel size to extract lung nodule features in the face of different fine-grained lung nodules. Second, the contextual supplementary (CS) block is designed to use the information of adjacent upper and lower layers to correct for the upper layer features, eliminating the discrepancy in the fusion of features at different levels. In this paper, the LUNA16 dataset was selected as the basis for lung nodule segmentation experiments. The method used in this dataset can achieve an intersection ratio (IoU) of 80.59% and a dice similarity coefficient (DSC) score of 89.25%. The network effectively improves the accuracy of lung nodule segmentation compared with other models. The results show that the method enhances the feature extraction ability of the network and improves the segmentation effect. In addition, the contribution of jump connections to information recovery should be noted.

Deep learning-based lung nodule detection: a review

CT scan acquisition is fast and cost-effective and has become the main lung imaging tool. However, the increase in large numbers of CT scans has placed a heavy burden on radiologists; therefore, automated lung nodule detection techniques are needed to reduce the workload of radiologists and computer-aided detection systems are proposed for further accurate diagnosis of the condition. This review provides a comprehensive overview of recent automated lung nodule detection techniques and challenges, etc., as well as a detailed overview and discussion of current research gaps, future developments, and research trends. Relevant articles published in databases such as IEEE Xplore, Science Direct, PubMed, and Web of Science cover research algorithms published from 2014 to 2023, mainly discussing deep learning-based techniques. The schemes presented in these articles, the databases used, the experimental results, and the performance of the algorithms are compared and discussed. This work aims to introduce researchers and readers to the latest techniques and their advances in the detection of lung nodules in the last decade, which will help researchers and radiologists to further understand the latest techniques in this field.

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data

The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor (EGFR) mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large. Additional challenges such as adequacy of tissue in case of lung core biopsies and locating suitable tumour tissues as a result of innate intratumoral heterogeneity indicate the necessity of an AI-based end-to-end pipeline capable of automatically detecting and learning more effective lung nodule features from CT images and predicting the probability of the EGFR-mutant. This will help the oncologists and patients in resource-limited settings to achieve near-optimal care and appropriate therapy. The EGFR gene sequencing and CT imaging data of 2277 patients with lung carcinoma were included from three cohorts in India and a White population cohort collected from TCIA. Another cohort LIDC-IDRI was used to train the AIPS-Nodule (AIPS-N) model for automatic detection and characterisation of lung nodules. We explored the value of combining the results of the AIPS-N with the clinical factors in the AIPS-Mutation (AIPS-M) model for predicting EGFR genotype, and it was evaluated by area under the curve (AUC). AIPS-N achieved an average AP50 of 70.19% in detecting the location of nodules within the lung region of interest during validation and predicted the score of five lung nodule properties. The AIPS-M machine learning (ML) and deep learning (DL) models achieved AUCs ranging from 0.587 to 0.910. The AIPS suggests that CT imaging combined with a fully automated lung-nodule analysis AI system can predict EGFR genotype and identify patients with an EGFR mutation in a cost-effective and non-invasive manner. This work was supported by a grant provided by Conquer Cancer Foundation of ASCO [2021IIG-5555960128] and Pfizer Products India Pvt. Ltd.

An automatic lung nodule detection and classification using an optimized convolutional neural network and enhanced k-means clustering

An automatic lung nodule detection and classification using an optimized convolutional neural network and enhanced k-means clustering

Artificial Intelligence in Lung Cancer Screening: The Future Is Now.

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

RETRACTED ARTICLE: Optimized convolutional neural network for automatic lung nodule detection with a new active contour segmentation

RETRACTED ARTICLE: Optimized convolutional neural network for automatic lung nodule detection with a new active contour segmentation

Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer

Lung cancer is the world’s second-largest cause of cancer mortality. Patients’ lives can be saved if this malignancy is detected early. Doctors, however, encounter difficulties in detecting cancer in computed tomography (CT) images. In recent years, significant research has been devoted to producing automated lung nodule detection methods that can help radiologists. Most of them use only the lung window in their analysis and generally do not consider the mediastinal windows, which, according to recent research, carry important information. In this paper, we propose a simple yet effective algorithm to analyze multi-window CT images for lung nodules. The algorithm works in three steps. First, the CT image is preprocessed to suppress any noise and improve the image quality. Second, the lungs are extracted from the preprocessed image. Based on the histogram analysis of the lung windows, we propose a multi-Otsu-based approach for lung segmentation in lung windows. The case of mediastinal windows is rather difficult due to irregular patterns in the histograms. To this end, we propose a global–local-mean-based thresholding technique for lung detection. In the final step, the nodule candidates are extracted from the segmented lungs using simple intensity-based thresholding. The radius of the extracted objects is computed to separate the nodule from the bronchioles and blood vessels. The proposed algorithm is evaluated on the benchmark LUNA16 dataset and achieves accuracy of over 94% for lung tumor detection, surpassing that of existing similar methods.

An Improved Convolution Neural Network and Modified Regularized K-Means-Based Automatic Lung Nodule Detection and Classification.

If lung cancer is not detected in its initial phases, it can be fatal. However, because of the quantity and structure of its nodules, lung cancer is difficult to detect early. For accurate detections, radiologists require assistance from automated tools. Numerous expert methods have been created over time to assist radiologists in the diagnosis of lung cancer. However, this requires accurate research. Therefore, in this article, we propose a framework to precisely detect lung cancer by categorizing it between benign and malignant nodules. To achieve this objective, an efficient deep-learning algorithm is presented. The presented technique consists of four stages, namely pre-processing, segmentation, classification, and severity stage analysis. Initially, the collected image is given to the pre-processing stage to eliminate the distortion present in the image. Then, the noise-free image is given to the segmentation stage. For segmentation, in this paper, modified regularized K-means (MRKM) clustering algorithm is presented. After the segmentation process, the segmented nodule image is fed to the classification stage to categorize the nodule as benign or malignant (risk nodule). For classification, an improved convolution neural network (ICNN) is presented. The proposed ICNN is designed by modifying CNN with the integration of the adaptive tree seed optimization (ATSO) algorithm. Finally, the stage identification is carried out based on the size of the nodule and we classify the malignant nodule as S1-S4. The presented technique attained the maximum accuracy of 96.5% and performance compared with existing state-of-art methods.

Controlling False-Positives in Automatic Lung Nodule Detection by Adding 3D Cuboid Attention to a Convolutional Neural Network

Background and ObjectiveFalse-positive diagnoses often occur during pulmonary nodule detection, which directly affects the prognosis of patients. The development of intelligent computing and the wide application of deep learning technology have brought about the intelligent detection of lung nodules, which has greatly improved the detection efficiency and detection accuracy. MethodsThis paper explores a convolutional neural network filled with a 3D cuboid attention module for lung nodule detection comprehensively encompassing nodules in the cross, coronal, and sagittal planes of CT sections. First, 3D cuboid attention module extracts image features in order from the plane, space, and volume directions of CT slices (PSV-AM). Second, the model adopts a structure named UPSV-Net of 9 modules to expose the lesion area as much as possible. The PSV-AM is embedded into the network to guide it in extracting the structural features of nodules. At the same time, the features extracted from the shallow and deep layers are associatively concatenated. Thirdly, on the basis of the convolution module shared from the detection branch, deepening the convolution path to form the true and false nodule classification network, which is committed to reducing the false positive rate. ResultsThe model study was validated on the 2016 Lung Nodule Analysis challenge dataset. On the test bench, the detection accuracy is 96.15%, and the sensitivity is 97.6%. Subsequently, the sensitivity to the true and false nodules reached 97.61% and the average false-positive detection rate (FPDR) was 0.893, which was comparable to the diagnosis results of clinicians.

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.

Robust and accurate pulmonary nodule detection with self-supervised feature learning on domain adaptation.

Medical imaging data annotation is expensive and time-consuming. Supervised deep learning approaches may encounter overfitting if trained with limited medical data, and further affect the robustness of computer-aided diagnosis (CAD) on CT scans collected by various scanner vendors. Additionally, the high false-positive rate in automatic lung nodule detection methods prevents their applications in daily clinical routine diagnosis. To tackle these issues, we first introduce a novel self-learning schema to train a pre-trained model by learning rich feature representatives from large-scale unlabeled data without extra annotation, which guarantees a consistent detection performance over novel datasets. Then, a 3D feature pyramid network (3DFPN) is proposed for high-sensitivity nodule detection by extracting multi-scale features, where the weights of the backbone network are initialized by the pre-trained model and then fine-tuned in a supervised manner. Further, a High Sensitivity and Specificity (HS) network is proposed to reduce false positives by tracking the appearance changes among continuous CT slices on Location History Images (LHI) for the detected nodule candidates. The proposed method's performance and robustness are evaluated on several publicly available datasets, including LUNA16, SPIE-AAPM, LungTIME, and HMS. Our proposed detector achieves the state-of-the-art result of sensitivity at false positive per scan on the LUNA16 dataset. The proposed framework's generalizability has been evaluated on three additional datasets (i.e., SPIE-AAPM, LungTIME, and HMS) captured by different types of CT scanners.

Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification.

Simple SummaryArtificial Intelligence (AI) algorithms can assist clinicians in their daily tasks by automatically detecting and/or classifying nodules in chest CT scans. Bias of such algorithms is one of the reasons why implementation of them in clinical practice is still not widely adopted. There is no published review on the bias that these algorithms may contain. This review aims to present different types of bias in such algorithms and present possible ways to mitigate them. Only then it would be possible to ensure that these algorithms work as intended under many different clinical settings.Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely.

Lung Nodule Segmentation using Deep Learning and Advanced UNet Model

Cancer is known as one of the world’s top reason of mortality in human beings. Lung cancer, notably, has the highest mortality rate. Thus, timely detection of nodule or tumor is a critical and significant job in saving lives. One of the hot topic in current research field is automatic detection of lung nodules. Many methods have been implemented using computer vision-based technologies in the past, but achieving the desired precision still remains a difficult job. In this research, we adopt Convolutional Neural Network (CNN) based UNet image segmentation model and improved its architecture by incorporating convolution mechanisms. Moreover, this scheme uses binary cross entropy as loss function during training process. The proposed mechanism is tested on LIDC-IDRI dataset. The experimental analysis shows the augmented performance of proposed approach when compared with existing segmentation techniques. The qualitative and quantitative comparative analysis shows that the suggested scheme substantially improves the efficiency of segmentation performance.

Channel-Wise Attention Mechanism in the 3D Convolutional Network for Lung Nodule Detection

Pulmonary nodule detection is essential to reduce the mortality of lung cancer. One-stage detection methods have recently emerged as high-performance and lower-power alternatives to two-stage lung nodule detection methods. However, it is difficult for existing one-stage detection networks to balance sensitivity and specificity. In this paper, we propose an end-to-end detection mechanism combined with a channel-wise attention mechanism based on a 3D U-shaped residual network. First, an improved attention gate (AG) is introduced to reduce the false positive rate by employing critical feature dimensions at skip connections for feature propagation. Second, a channel interaction unit (CIU) is designed before the detection head to further improve detection sensitivity. Furthermore, the gradient harmonizing mechanism (GHM) loss function is adopted to solve the problem caused by the imbalance of positive and negative samples. We conducted experiments on the LUNA16 dataset and achieved a performance with a competition performance metric (CPM) score of 89.5% and sensitivity of 95%. The proposed method outperforms existing models in terms of sensitivity and specificity while maintaining the attractiveness of being lightweight, making it suitable for automatic lung nodule detection.

Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening.

Artificial Intelligence (AI)-based automatic lung nodule detection system improves the detection rate of nodules. It is important to evaluate the clinical value of the AI system by comparing AI-assisted nodule detection with actual radiology reports. To compare the detection rate of lung nodules between the actual radiology reports and AI-assisted reading in lung cancer CT screening. Participants in chest CT screening from November to December 2019 were retrospectively included. In the real-world radiologist observation, 14 residents and 15 radiologists participated in finalizing radiology reports. In AI-assisted reading, one resident and one radiologist reevaluated all subjects with the assistance of an AI system to locate and measure the detected lung nodules. A reading panel determined the type and number of detected lung nodules between these two methods. In 860 participants (57±7 years), the reading panel confirmed 250 patients with >1 solid nodule, while radiologists observed 131, lower than 247 by AI-assisted reading (p<0.001). The panel confirmed 111 patients with >1 non-solid nodule, whereas radiologist observation identified 28, lower than 110 by AI-assisted reading (p<0.001). The accuracy and sensitivity of radiologist observation for solid nodules were 86.2% and 52.4%, lower than 99.1% and 98.8% by AI-assisted reading, respectively. These metrics were 90.4% and 25.2% for non-solid nodules, lower than 98.8% and 99.1% by AI-assisted reading, respectively. Comparing with the actual radiology reports, AI-assisted reading greatly improves the accuracy and sensitivity of nodule detection in chest CT, which benefits lung nodule detection, especially for non-solid nodules.

SCPM-Net: An anchor-free 3D lung nodule detection network using sphere representation and center points matching.

Automatic and accurate lung nodule detection from 3D Computed Tomography (CT) scans plays a vital role in efficient lung cancer screening. Despite the state-of-the-art performance obtained by recent anchor-based detectors using Convolutional Neural Networks (CNNs) for this task, they require predetermined anchor parameters such as the size, number, and aspect ratio of anchors, and have limited robustness when dealing with lung nodules with a massive variety of sizes. To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network (SCPM-Net) that is anchor-free and automatically predicts the position, radius, and offset of nodules without manual design of nodule/anchor parameters. The SCPM-Net consists of two novel components: sphere representation and center points matching. First, to match the nodule annotation in clinical practice, we replace the commonly used bounding box with our proposed bounding sphere to represent nodules with the centroid, radius, and local offset in 3D space. A compatible sphere-based intersection over-union loss function is introduced to train the lung nodule detection network stably and efficiently. Second, we empower the network anchor-free by designing a positive center-points selection and matching (CPM) process, which naturally discards pre-determined anchor boxes. An online hard example mining and re-focal loss subsequently enable the CPM process to be more robust, resulting in more accurate point assignment and mitigation of class imbalance. In addition, to better capture spatial information and 3D context for the detection, we propose to fuse multi-level spatial coordinate maps with the feature extractor and combine them with 3D squeeze-and-excitation attention modules. Experimental results on the LUNA16 dataset showed that our proposed SCPM-Net framework achieves superior performance compared with existing anchor-based and anchor-free methods for lung nodule detection with the average sensitivity at 7 predefined FPs/scan of 89.2%. Moreover, our sphere representation is verified to achieve higher detection accuracy than the traditional bounding box representation of lung nodules. Code is available at:https://github.com/HiLab-git/SCPM-Net.