Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

Riquelme, Diego; Akhloufi, Moulay A.

doi:10.3390/ai1010003

Cited by 119 publications

(52 citation statements)

References 78 publications

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“…This issue has been tackled by applying data augmentation techniques. The most widely used are translations, rotations, resizing, flipping and cropping patches [ 34 ]. There have also been more sophisticated approaches.…”

Section: Introductionmentioning

confidence: 99%

Lung Nodule Segmentation with a Region-Based Fast Marching Method

Savić

Ramponi

et al. 2021

Sensors

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When dealing with computed tomography volume data, the accurate segmentation of lung nodules is of great importance to lung cancer analysis and diagnosis, being a vital part of computer-aided diagnosis systems. However, due to the variety of lung nodules and the similarity of visual characteristics for nodules and their surroundings, robust segmentation of nodules becomes a challenging problem. A segmentation algorithm based on the fast marching method is proposed that separates the image into regions with similar features, which are then merged by combining regions growing with k-means. An evaluation was performed with two distinct methods (objective and subjective) that were applied on two different datasets, containing simulation data generated for this study and real patient data, respectively. The objective experimental results show that the proposed technique can accurately segment nodules, especially in solid cases, given the mean Dice scores of 0.933 and 0.901 for round and irregular nodules. For non-solid and cavitary nodules the performance dropped—0.799 and 0.614 mean Dice scores, respectively. The proposed method was compared to active contour models and to two modern deep learning networks. It reached better overall accuracy than active contour models, having comparable results to DBResNet but lesser accuracy than 3D-UNet. The results show promise for the proposed method in computer-aided diagnosis applications.

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Section: Introductionmentioning

confidence: 99%

Lung Nodule Segmentation with a Region-Based Fast Marching Method

Savić

Ramponi

et al. 2021

Sensors

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“…Meanwhile, various authors have reported automatic lung nodule detection algorithms using deep learning 19 . In the effort to minimize false negatives and FPs, Wang et al proposed a nodule‐size‐adaptive model that can measure the nodule sizes, types, and locations from three‐dimensional (3D) images 20 .…”

Section: Introductionmentioning

confidence: 99%

“…18 Meanwhile, various authors have reported automatic lung nodule detection algorithms using deep learning. 19 In the effort to minimize false negatives and FPs, Wang et al proposed a nodule-size-adaptive model that can measure the nodule sizes, types, and locations from three-dimensional (3D) images. 20 Moreover, Dou et al used 3D convolutional neural networks to extract multilevel contextual information to reduce FPs, 21 while Xie et al utilized two-dimensional (2D) convolutional neural networks for FP reduction.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification

et al. 2020

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Purpose: Early detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes. Methods: The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder-decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a threedimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non-nodules. In the public LIDC-IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross-validation scheme. The free-response receiver operating characteristic curve is used for performance assessment. Results: The proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane. Conclusion: Our approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.

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“…In [1], Deep learning (DL) frameworks are used as they extract features deep down the hidden network with variable factors acquired during training. Most medical imaging models use Convolution Neural Networks (CNN) for prediction.…”

Section: Iintroductionmentioning

confidence: 99%

Optimising Classification Techniques for Lung Cancer Detection on Ct Images

Peeris¹,

Brundha²

2020

EPRA

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Lungs are the most crucial organs in a human body. Since the cancer detection began, lung cancer has been the most common terminal disease amongst all type of cancers. The contribution of deep learning, especially the convolution neural networks has widely reduced the mortality rates resulting from lung cancer. The classification of Computed Tomography (CT) images has enhanced the early diagnosis of lung cancer that has enabled victims to undergo treatment at an early stage. The resolution of the CT images have been variedly used for the accuracy of the model. Besides, the detection of lumps or anomalies in the images has greatly supported early diagnosis. Classification plays a vital role in the deep learning models to sort out the input images as positive and negative based on the attribute of the model built. However, the generalisation of classifiers has reduced the accuracy of the corresponding models built. To increase the accuracy and efficiency of the deep learning model, an optimised classification technique is used to predict lung cancer from the CT images. The purpose of optimisation here will enable the model to adapt stipulated feature extraction process according to the input images fed into the network. The model will be trained for predicting purpose given any resolution of the images. KEYWORDS: Lung cancer, CT images, Classification techniques, Optimised Classification, Prediction

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Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

Cited by 119 publications

References 78 publications

Lung Nodule Segmentation with a Region-Based Fast Marching Method

Lung Nodule Segmentation with a Region-Based Fast Marching Method

Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification

Optimising Classification Techniques for Lung Cancer Detection on Ct Images

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