Lung Nodule Classification Using Combined Deep and Spectral 3D Shape Features

Bashiri, Fereshteh S.; Badger, Jonathan C.; D’Souza, Roshan; Yu, Zeyun; Peissig, Peggy L.

doi:10.1109/bhi.2019.8834537

Cited by 1 publication

(1 citation statement)

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“…The extracted features are classified using Naïve Bayes, J48, and Bayes Net classifiers. While Fereshteh S Bashiri et al in [26] used deep and spectral 3D shape features for the classification of lung nodules by utilizing the entire CT images. The proposed spectral analysis aimed for the extraction of a compact and comprehensive information vector by analyzing the surface structure of lung nodules.…”

Section: A Classification Based On Entire Input Imagementioning

confidence: 99%

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

et al. 2021

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Interpreting chest x-ray (CXR) to find anomalies in the thoracic region is a tedious job and can consume an ample amount of radiologist's time when there are thousands of them to process. In such scenarios, the Computer-Aided Diagnostic (CAD) systems can help radiologists by doing the trivial processing and presenting the information in a meaningful way so that, the radiologist can make more accurate decisions by spending less amount of time and energy. This research study intends to propose a better, accurate, and efficient CNN based pulmonary disease diagnosis system using CXR images. In the proposed system, the capabilities of deep neural network architecture are exploited by proposing a custom CNN architecture with additional layers and modified hyperparameters to meet the required results. The input CXR is examined for healthy or infected at the surface level and the infected images are further processed for class level label classification. The lung region is segmented from the entire input CXR image to reduce the amount of noise and increase the processing efficiency by processing less overall information. The proposed model is evaluated on the benchmark split of the NIH chest x-ray dataset and achieves better segmentation and classification results when compared to the state of the art approaches.

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Section: A Classification Based On Entire Input Imagementioning

confidence: 99%