FEATURE-OPTIMIZED DEEP LEARNING MODEL FOR LIVER CT SCANS VIA SOCIAL SPIDER OPTIMIZATION

Liver diseases such as fatty liver, cirrhosis, and hepatitis are becoming increasingly widespread due to factors like poor diet, excessive alcohol consumption, and sedentary lifestyles.Given the liver's vital role in human physiology, early detection of such conditions is critical to reducing the high mortality rates associated with liver dysfunction.Traditional diagnostic techniques, including biopsy, X-ray, CT, and MRI scans, aid in the early identification of liver-related abnormalities.This research introduces an advanced framework for liver disease detection utilizing artificial intelligence (AI) and pre-trained deep learning models.The study primarily focuses on CT scan images for liver disease classification.To enhance computational efficiency and improve model accuracy, the Social Spider Optimization (SSO) algorithm is implemented for feature selection and dimensionality reduction from the CT images.Following feature extraction and reduction via SSO, three established Convolutional Neural Networks (CNNs) are employed to classify the liver images.These CNN models are trained and finetuned using the optimized feature sets produced by the SSO algorithm.A publicly accessible liver CT image dataset is used to assess the performance of the proposed models through various evaluation metrics.This structured approach enables an in-depth analysis of the influence of feature reduction on classification outcomes.By integrating SSO-based feature optimization with powerful pre-trained CNN architectures, the proposed method demonstrates the potential of AI-enhanced tools in medical imaging and accurate liver disease diagnosis.