Liver Lesion Detection Using Semantic Segmentation and Chaotic Cuckoo Search Algorithm

Abstract

The classic feature extraction techniques used in recent research on computer-aided diagnosis (CAD) of liver cancer have several disadvantages, including duplicated features and substantial computational expenses. Modern deep learning methods solve these issues by implicitly detecting complex structures in massive quantities of healthcare image data. This study suggests a unique bio-inspired deep-learning way for improving liver cancer prediction outcomes. Initially, a novel semantic segmentation technique known as UNet++ is proposed to extract liver lesions from computed tomography (CT) images. Second, a hybrid approach that combines the Chaotic Cuckoo Search algorithm and AlexNet is indicated as a feature extractor and classifier for liver lesions. LiTS, a freely accessible database that contains abdominal CT images, was employed for liver tumor diagnosis and investigation. The segmentation results were evaluated using the Dice similarity coefficient and Correlation coefficient. The classification results were assessed using Accuracy, Precision, Recall, F1 Score, and Specificity. Concerning the performance metrics such as accuracy, precision, and recall, the recommended method performs better than existing algorithms producing the highest values such as 99.2%, 98.6%, and 98.8%, respectively.