Enhanced WOA and Modular Neural Network for Severity Analysis of Tuberculosis

Abstract

Generally, Tuberculosis (TB) is an extremely infectious disease and it is a significant medical issue everywhere throughout the globe. The exact recognition of TB is the main concern faced by the majority of conventional algorithms. Hence, this paper addresses these problems and presented a successful method for recognizing TB utilizing the modular neural network. Moreover, for transforming the RGB image to LUV space, the color space transformation is utilized. At that point, adaptive thresholding is done for image segmentation and several features, such as density, coverage, color histogram, length, area, and texture features, are extracted to enable effectual classification. Subsequent to the feature extraction, the size of the features is decreased by exploiting Principal Component Analysis (PCA). For the classification, the extracted features are exposed to Whale Optimization Algorithm-based Convolutional Neural Network (WOA-CNN). Subsequently, the image level features, such as bacilli area, bacilli count, scattering coefficients and skeleton features are considered to do severity detection utilizing proposed Enhanced Whale Optimization Algorithm-based Modular Neural Network (EWOA-MNN). In conclusion, the inflection level is resolved to utilize density, entropy, and detection percentage. The proposed method is modeled by enhancing the WOA method.