Particle swarm optimization‐based liver disorder ultrasound image classification using multi‐level and multi‐domain features

Abstract

Liver ultrasound is a cost-effective, non-invasive, and sufficient technique to diagnose most of the liver disorders. The recent advancements in research in image processing have led to the development of image-based liver disorder classification systems. In spite of being popular in the diagnostic imaging of liver, ultrasound images, owing to their poor quality, render the conventional and state of the art segmentation and feature extraction techniques incapable, to accurately classify a large mixed group of liver disorders; due to the similarities and differences in appearances among the different and same disorders, respectively. Classification of liver disorders using ultrasound images poses various challenges at each phase, from segmentation to classification. There is a need for better segmentation, powerful features, and optimal classification parameter combinations to obtain decent classification accuracy, when a large sub-set of liver disorders is considered. In this work, the region of interest is extracted using iso-contour technique. Feature extraction is performed using multi-level fractal features and multi-domain wavelet-texture features for better discrimination capability. Then, an optimization problem is formulated, for minimizing the five fold cross validation error to classify 10 types of disorders, both focal and diffused, by selecting the best features, suitable classifier, and its parameters using the particle swarm optimization technique for obtaining better classification. An overall accuracy of 91% is obtained using the proposed features in addition to 50% reduction in multi-level fractal feature set which justifies the efficacy of the proposed technique.

Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia

“…Usage of segmentation is another important feature in this work. It creates a space for researchers to pave a path for the interesting challenge at each phase from segmentation towards classification [ 33 ]. Ohata et al derived a technique for automatic detection of COVID-19 infection using CXR images through transfer learning [ 34 ].…”