An Efficient Discrete Wavelet Transform Based Partial Hadamard Feature Extraction and Hybrid Neural Network Based Monarch Butterfly Optimization for Liver Tumor Classification
Abstract
The liver tumor is one of the most widely occurring cancers nowadays. There are several forms of liver tumors, which are most often caused by hepatitis and cirrhosis. Furthermore, metastatic liver cancer may spread to other organs, posing a serious health risk. Hence it is ineluctable to diagnose this intimidating problem as early as possible. Liver tumour classification from ultrasound images is a challenging task since it is based on the structure and orientation of the liver tumour cells. To overcome this challenge, a novel hybrid artificial neural network-based monarch butterfly optimization algorithm is proposed for accurate liver tumour classification. Before the classification process, the liver tumor cells are preprocessed using different techniques such as adaptive filtering, median filtering, and color to greyscale transformation. Then the pre-processed images are segmented using the adaptively regularized kernel-based fuzzy C-means clustering algorithm and level enhanced segmentation which enhances the segmentation process and the same features are aligned in the same segment. Further, the feature vectors are extracted with the aid of the hybrid Discrete Wavelet Transform-based partial Hadamard transform method, and the same features are mapped as the same vector. The classification task is performed by a hybrid artificial neural network-based monarch butterfly optimization algorithm which enhances the classification accuracy. The comparative analyses with the state-of-art works show that the proposed work outperforms all the other approaches in terms of accuracy, specificity, sensitivity, precision, recall, and F1-score.