Liver cancer is a significant global health concern, with its prevalence steadily rising over the years. The accurate detection and classification of liver cancer are pivotal for timely treatment and improved patient ...
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Liver cancer is a significant global health concern, with its prevalence steadily rising over the years. The accurate detection and classification of liver cancer are pivotal for timely treatment and improved patient outcomes. The most challenging tasks identified from the previous research studies are computational complexity, sensitive parameter setting, misdetection and misclassification. So, a deep learning-basedoptimizationalgorithm is proposed to detect and classify liver cancer. The image data are collected from the LiTS17 dataset, 3D-IRCADb dataset and Liver tumor CT dataset to preprocess the medical image data and the preprocessing provides consistency, quality and privacy. The differential Convolutional Neural Network (differential CNN) model extracts the relevant features for improving the ability of a model to differentiate healthy and cancerous tissues. The features are classified into benign and malignant by using the classification model namely Kernel Extreme Learning Machine (KELM) model. The differential biogeography-based optimization algorithm (DBBOA) algorithm fine-tunes the parameters to find near-optimal solutions. This tuning process is conducted during training the deep learning-based classification model. The experimental validation is conducted in terms of using significant performance evaluation measures and the comprehensive analysis provided a better classification accuracy of 98.72%, F1-score of 98.25%, specificity of 97.93%, sensitivity of 98.52%, AUCROC of 0.9872, precision of 98.89% and a computational time of 1.5 s for the proposed liver cancer detection and classification model. The comparative analysis showed that the proposed model achieved a superior outcome rather than other existing methods.
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