CT-LIVERNET: AN EFFICIENT DEEP LEARNING MODEL FOR LIVER LESION SEGMENTATION AND DIAGNOSIS

Medicine has undergone substantial changes because of big data analytics combined with deep learning technologies which enable doctors to predict diseases better while monitoring patients and diagnosing conditions as well as delivering better treatments. Liver disease stands as a worldwide significant health challenge attributed to its multiple complicated aspects and elevated death rates as well as its multiple pathologic expressions. A combination of wrong medical diagnosis and insufficient medical care decreases survival expectancy dramatically. Detecting liver disease presents a difficult issue because some damaged liver areas can look unblemished due to minimal lesion distribution which causes experts to make incorrect classifications. The assessment of liver conditions with precision at an early stage proves essential for shrinking treatment deficiencies along with enhanced medical results. The progress of deep learning and medical imaging allowed researchers to develop new detection procedures for liver disease analysis. The presented research develops a novel approach to liver disease recognition that adopts YOLO (You Only Look Once) as its feature extractor and pairs this with Random Forest (RF) and XGBoost classifiers for superior classification accuracy. The proposed diagnostic methodology reached a 95% accuracy level which proved superior to Support Vector Machine (SVM) and Logistic Regression and yielded optimal diagnosis and classification results.