Pulmonary Lesion Classification Framework Using the Weighted Ensemble Classification with Random Forest and CNN Models for EBUS Images
Open Access
- 26 June 2022
- journal article
- research article
- Published by MDPI AG in Diagnostics
- Vol. 12 (7), 1552
- https://doi.org/10.3390/diagnostics12071552
Abstract
Lung cancer is a deadly disease with a high mortality rate. Endobronchial ultrasonography (EBUS) is one of the methods for detecting pulmonary lesions. Computer-aided diagnosis of pulmonary lesions from images can help radiologists to classify lesions; however, most of the existing methods need a large volume of data to give good results. Thus, this paper proposes a novel pulmonary lesion classification framework for EBUS images that works well with small datasets. The proposed framework integrates the statistical results from three classification models using the weighted ensemble classification. The three classification models include the radiomics feature and patient data-based model, the single-image-based model, and the multi-patch-based model. The radiomics features are combined with the patient data to be used as input data for the random forest, whereas the EBUS images are used as input data to the other two CNN models. The performance of the proposed framework was evaluated on a set of 200 EBUS images consisting of 124 malignant lesions and 76 benign lesions. The experimental results show that the accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve are 95.00%, 100%, 86.67%, 92.59%, 100%, and 93.33%, respectively. This framework can significantly improve the pulmonary lesion classification.This publication has 44 references indexed in Scilit:
- Histogram-Based Quantitative Evaluation of Endobronchial Ultrasonography Images of Peripheral Pulmonary LesionRespiration, 2015
- Lung Cancer ScreeningAmerican Journal of Respiratory and Critical Care Medicine, 2015
- Endobronchial Ultrasound Elastography in the Diagnosis of Mediastinal and Hilar Lymph NodesJapanese Journal of Clinical Oncology, 2014
- Mutual Information between Discrete and Continuous Data SetsPLOS ONE, 2014
- A random forest classifier for lymph diseasesComputer Methods and Programs in Biomedicine, 2014
- Methods for Staging Non-small Cell Lung CancerSocial psychiatry. Sozialpsychiatrie. Psychiatrie sociale, 2013
- The reasons of false negative results of endobronchial ultrasound-guided transbronchial needle aspiration in the diagnosis of intrapulmonary and mediastinal malignancy.Thoracic Cancer, 2013
- Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient CohortsPLoS Computational Biology, 2011
- Image enhancement by modified contrast-stretching manipulationOptics & Laser Technology, 2006
- A methodology for evaluation of boundary detection algorithms on medical imagesIEEE Transactions on Medical Imaging, 1997