A multi-task CNN approach for lung nodule malignancy classification and characterization

Marques, Sónia; Schiavo, Filippo; Ferreira, Carlos; Pedrosa, João; Cunha, António; Campilho, Aurélio

doi:10.1016/j.eswa.2021.115469

Cited by 25 publications

(11 citation statements)

References 16 publications

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“…Four different architectures for malignancy classification and nodule characterization had further been attempted. 19 In this study, the first architecture takes nodule features as input, and predicts malignancy; second architecture takes CT image as input and predicts malignancy; third and fourth architecture takes CT image as input and predicts malignancy and nodule features. They ignored the prediction of internal structure of nodule.…”

Section: Dnn-based Attributes and Malignancy Classification Tasksmentioning

confidence: 99%

An ensemble of three-dimensional deep neural network models for multi-attribute scoring and classification of pulmonary nodules

Sakshiwala

Singh

2023

Proc Inst Mech Eng H

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Lung cancer is the uncontrolled growth of cells that originates in the lung parenchyma or cells that line the air passages. These cells divide rapidly to form malicious tumors. This paper proposes a multi-task ensemble of three dimensional (3D) deep neural network (DNN) based model, namely: pre-trained EfficientNetB0, BiGRU-based SEResNext101, and the proposed LungNet. The ensemble model performs binary classification and regression tasks to accurately classify the benign and malignant pulmonary nodules. This study also explores the attribute importance and proposes a domain knowledge-based regularization technique. The proposed model is evaluated on the public benchmark LIDC-IDRI dataset. Through a comparative study, it was shown that when coefficients generated by the random forest (RF) are used in the loss function, the proposed ensemble model offers a better prediction capability of the accuracy of 96.4% compared to the state-of-the-art methods. In addition, the receiver operating characteristic curves show that the proposed ensemble model has better performance than the base learners. Thus, the proposed CAD-based model can efficiently detect malignant pulmonary nodules.

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Section: Dnn-based Attributes and Malignancy Classification Tasksmentioning

confidence: 99%

An ensemble of three-dimensional deep neural network models for multi-attribute scoring and classification of pulmonary nodules

Sakshiwala

Singh

2023

Proc Inst Mech Eng H

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“…Marques et al [ 177 ] developed a multi-task CNN to classify malignancy nodules with an AUC of 0.783. Thamilarasi et al [ 178 ] proposed an automatic lung nodule classifier based on CNN with an accuracy of 86.67% for the JSRT dataset.…”

Section: Lung Cancer Prediction Using Deep Learningmentioning

confidence: 99%

Deep Learning Techniques to Diagnose Lung Cancer

Wang

2022

Cancers

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Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

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“…To solve this problem, Liu et al 7 presented a 3D CNN to automatically extract lung nodule spatial features for benign-malignant lung nodule classification. Marques et al 8 based on the CT images and semantic features of nodules proposed a multi-task 3D CNN model. Li et al 9 and Zhang et al 10 used a 3D CNN to extract the deep-learning features and combined the radiomics features to form the input of a classifier to realize the classification task of benign and malignant lung nodules.…”

Section: Introductionmentioning

confidence: 99%

“…presented a 3D CNN to automatically extract lung nodule spatial features for benign‐malignant lung nodule classification. Marques et al 8 . based on the CT images and semantic features of nodules proposed a multi‐task 3D CNN model.…”

Section: Introductionmentioning

confidence: 99%

3D multi‐view squeeze‐and‐excitation convolutional neural network for lung nodule classification

Yang

et al. 2023

Medical Physics

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Early screening is crucial to improve the survival rate and recovery rate of lung cancer patients. Computer-aided diagnosis system (CAD) is a powerful tool to assist clinicians in early diagnosis. Lung nodules are characterized by spatial heterogeneity.However,many attempts use the two-dimensional multiview (MV) framework to learn and simply integrate multiple view features. These methods suffer from the problems of not capturing the spatial characteristics effectively and ignoring the variability of multiple views. In this paper, we propose a three-dimensional MV convolutional neural network (3D MVCNN) framework and embed the squeeze-and-excitation (SE) module in it to further address the variability of each view in the MV framework. Methods: First, the 3D multiple view samples of lung nodules are extracted by the spatial sampling method, and a 3D CNN is established to extract 3D abstract features. Second, build a 3D MVCNN framework according to the 3D multiple view samples and 3D CNN. This framework can learn more features of different views of lung nodules, taking into account the characteristics of spatial heterogeneity of lung nodules. Finally, to further address the variability of each view in the MV framework, a 3D MVSECNN model is constructed by introducing a SE module in the feature fusion stage. For training and testing purposes we used independent subsets of the public LIDC-IDRI dataset. Results: For the LIDC-IDRI dataset, this study achieved 96.04% accuracy and 98.59% sensitivity in the binary classification, and 87.76% accuracy in the ternary classification, which was higher than other state-of -the-art studies. The consistency score of 0.948 between the model predictions and pathological diagnosis was significantly higher than that between the clinician's annotations and pathological diagnosis. Conclusions:The results show that our proposed method can effectively learn the spatial heterogeneity of nodules and solve the problem of multiple view variability. Moreover, the consistency analysis indicates that our method can provide clinicians with more accurate results of benign-malignant lung nodule classification for auxiliary diagnosis, which is important for assisting clinicians in clinical diagnosis.

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A multi-task CNN approach for lung nodule malignancy classification and characterization

Cited by 25 publications

References 16 publications

An ensemble of three-dimensional deep neural network models for multi-attribute scoring and classification of pulmonary nodules

An ensemble of three-dimensional deep neural network models for multi-attribute scoring and classification of pulmonary nodules

Deep Learning Techniques to Diagnose Lung Cancer

3D multi‐view squeeze‐and‐excitation convolutional neural network for lung nodule classification

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