Deep learning for screening of interstitial lung disease patterns in high-resolution CT images

Agarwala, Sunita; Kale, Mandar; Kumar, Deepak; Swaroop, R.; Kumar, Avneesh; Dhara, Ashis Kumar; Thakur, Sumitra Basu; Sadhu, Anup; Nandi, Debashis

doi:10.1016/j.crad.2020.01.010

Cited by 36 publications

(12 citation statements)

References 14 publications

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“…To date, several studies have shown success with TA and ML to classify ILD patterns, estimate disease extent and to track treatment efficacy. 28,[38][39][40][41][42] Both traditional radiomics-based ML and deep neural networks have been shown to improve interobserver agreement and accuracy for radiologic ILD assessment. 43…”

Section: And Dl-based Methodsmentioning

confidence: 99%

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Suman

Koo

2023

Journal of Thoracic Imaging

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Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.

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Section: And Dl-based Methodsmentioning

confidence: 99%

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Suman

Koo

2023

Journal of Thoracic Imaging

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“…DenseNet121 gave a superior performance with an area under the curve (AUC) value of 80.97%. Agarwala et al 29 built a convolutional network for the identification of ILD images from the MedGIFT database and a clinical database. Fibrosis, emphysema and consolidation were classified.…”

Section: Literature Reviewmentioning

confidence: 99%

Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases

Thorat

Salvi

Dedhia

et al. 2022

Int J Imaging Syst Tech

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Interstitial lung diseases (ILDs) are adverse disorders, damaging the lung tissues, thus making timely diagnosis imperative. To counter the scarcity of publicly available high‐resolution computed tomography (HRCT) data, architecture employing HRCT and x‐ray data have been proposed for diagnosing ILDs. A model is first trained to diagnose three ILDs and a healthy lung from x‐ray data and then with a small amount of HRCT data of the same four classes. We introduce an EfficientNet+AlexNet model and a custom deep convolutional neural network and compare their performances with different weight initialization at the start of x‐ray image training. The EfficientNet+AlexNet model initialized with ImageNet weights performed best on the x‐ray dataset and was later used for domain adaptation. We also compare our model's performance to a single pre‐trained EfficientNetB0 model (trained on the same HRCT data). Our model gave an accuracy of 97.4% on the testing dataset, a notable increase in the performance compared to the recently proposed methodologies. The experimental results and graphical plots depict the superiority of domain adaptation and the potentials of weights that are a starting point for training the models.

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“…Agarwala et al [30] utilized a fully CNN network to detect the ILD patterns analyzing a chest HRCT segment. To acquire database-specific characteristics, the pre-trained model was built using the well-known PASCAL VOC database and fine-tuned using the MedGIFT ILD database, which was used to fine-tune the model.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

2021

Turk J Elec Eng & Comp Sci

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The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model. A comparison study showed that the presented model outperformed pre-trained CNNs and five-fold cross-validation on the same dataset. For ILDs pattern classification, the proposed approach achieved the accuracy scores of 99.09% and the average F score of 97.9% that outperforms three pre-trained CNNs. These outcomes show that the proposed model is relatively state-of-the-art in precision, recall, f score, and accuracy.

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Deep learning for screening of interstitial lung disease patterns in high-resolution CT images

Cited by 36 publications

References 14 publications

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases

An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

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