Tuberculosis Detection using X-Ray Images based on DbneAlexNet with Tangent Chef Leader Optimization
Keywords:
Chest X-ray images, Chef Based Optimization, DbneAlexNet, Double-Net, Hybrid Leader-Based OptimizationAbstract
Tuberculosis (TB) is a part of lung infection, which arises by the bacterial infectivity and it causes the major death in the globe. The precise and early detection of TB is necessary, if not, it may perhaps too severe. Presently, the TB detection process by chest X-ray (CXR) images has some difficulties to set up in low-cost embedded devices and personal computer. To overcome this issue, the optimization enabled DbneAlexNet based TB detection is proposed in this research. At the beginning stage, the input CXR image is applied to image pre-processing, in which the adaptive wiener filter (AWF) is hired to diminish the noise. Here, the Double-Net is adopted for the segmentation of TB affected region from the CXR image. Moreover, the parameters of Double-Net are trained by the proposed Chef Leader Based Optimization (CLBO). Furthermore, feature extraction process is accomplished for extracting the relevant features. At last, the TB disorder is detected by the DbneAlexnet, where the proposed Tangent Chef Leader Based Optimization (TCLBO) is designed for the training of DbneAlexnet. Furthermore, the TB detection is evaluated in connection with the metrics such as accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), True Positive Rate (TPR) and True Negative Rate (TNR) with the superior values like 0.991, 0.964, 0.958 0.993, and 0.968 are observed.
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