Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

Abstract

Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

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Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

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

“…In Li et al [33] , the author has leveraged the class and annotation information to train the CNN model and extract the features from local patch grids to detect and localize abnormalities. The author in [34] proposed an novel approach for identification of secondary pulmonary tuberculosis using Pseudo Zernike moment feature extractor and deep stacked sparse autoencoder and achieved an accuracy of 93.23% ± 0.81% and F 1 score of 93.23% ± 0.83%. Another work [35] presents rotation angle vector grid-based fractional Fourier entropy and deep stacked sparse autoencoder for accurate recognition of tuberculosis.…”