Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification

Chandra, Tej Bahadur; Singh, Bikesh Kumar; Jain, Deepak

doi:10.1016/j.cmpb.2022.106947

Cited by 21 publications

(8 citation statements)

References 72 publications

(128 reference statements)

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“…The model showed an AUC of 0.969, slightly higher than better than that used clinical parameters or images only. We further incorporated Friedman’s statistical test and post hoc multiple comparisons into the analysis for more accurate comparison between these differences referring to the methods used in the previous studies [ 41 , 42 ]. Friedman test was performed using the scipy library, resulting in a Friedman Statistic of 9.851 and a corresponding p-value of 0.00725, indicating significant differences among the groups.…”

Section: Resultsmentioning

confidence: 99%

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters

Zou,

Ren,

Yang

et al. 2024

BMC Pulm Med

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Background Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients. Methods In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads. Results The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively. Conclusion The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.

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Section: Resultsmentioning

confidence: 99%

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters

Zou,

Ren,

Yang

et al. 2024

BMC Pulm Med

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“…The Dice coefficient [ 51 ] and intersection over union (IoU) [ 52 ] were used as evaluation indices for segmentation. Here, represents the region predicted by the segmentation model; shows the region of ground truth.…”

Section: Methodsmentioning

confidence: 99%

Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks

et al. 2023

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Background This study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor’s point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities. Methods The model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN’s region of interest, we applied it to evaluation of the proposed model. Results Operation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images. Conclusions The proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment.

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“…Atanassov's intuitionistic fuzzy sets introduced a segmentation technique 13 . It focused on constructing membership functions for interval‐valued fuzzy sets by using Gaussian noise reduction 21 . A multi‐stage superpixel classification method was proposed for localizing a disease and assessing its severity from chest X‐ray images.…”

Section: Related Workmentioning

confidence: 99%

“…13 It focused on constructing membership functions for interval-valued fuzzy sets by using Gaussian noise reduction. 21 A multi-stage superpixel classification method was proposed for localizing a disease and assessing its severity from chest X-ray images. It employed a TB detection method in chest radiographs by employing a CNN framework.…”

Section: Review On Fuzzy Based Machine Learning Techniquesmentioning

confidence: 99%

Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods

Muthukumar,

Prasad,

Raju

et al. 2024

Int J Imaging Syst Tech

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Lung cancer is a deadly disease, and its early detection is crucial for effective treatment. In this context, accurate classification of lung cancers from computed tomography imaging is a vital research area. Irregularly detected gray matter in these images can affect classification outcomes, making accurate lung cancer detection difficult. To address this issue, researchers have developed a new approach that combines fuzzy logic and deep neural networks (DNNs) for extracting the hidden characteristic features of lung diseases from the images, and thereby, ensuring that only relevant features are used for classification. The proposed gray‐level fuzzy approach on DNNs (GL‐FDNN) is designed to accurately classify four distinguished lung cancer classes namely, Large cell carcinoma, Adenocarcinoma, Normal lung computed tomography, and Squamous cell carcinoma. After identifying the area of interest, its pixel intensity ratio is used to derive the fuzzy logic, which is then used to extract the most obscure gray matter features. Then, ResNet‐18 sorts the obscure features into categories and picks only relevant features to improve classification accuracy. The gray‐level fuzzy approach on DNNs technique was tested alongside some of the renowned, existing techniques for their relative performances. Experimental analysis was carried out on standard Kaggle datasets and the outcomes reveal that the proposed technique offers the highest level of lung disease classification accuracy (99.2%). It also improves the recall and precision factors. Thus, it can serve as a valuable diagnosis tool that can enhance the detection of lung cancer and the effectiveness of its treatment to save lives.

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Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification

Cited by 21 publications

References 72 publications

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters

Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks

Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods

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