Recognition of Early Liver Disease Detection using Deep Learning

Early and correct detection of liver diseases— including liver tumors, cirrhosis, and fibrosis—allows early medical intervention and improves patient outcomes utilizing suitable treatment. Conventional diagnostic methods frequently time-consuming, invasive, and prone to human error are manual radiological analysis and biopsies. This paper proposes an artificial intelligence-driven liver disease detection system to use deep learning and machine learning techniques to increase the accuracy and efficiency of liver disease diagnostics. Exact liver tumor segmentation from CT, MRI, and ultrasonic images using U-Net assures a correct delineation of affected regions in the proposed method. ResNet-50 is also utilized for liver abnormal categorization including fibrosis and cirrhosis as well as feature extraction. The method consists on thorough photo preprocessing comprising augmentation, noise reduction, and contrast enhancement (CLAHE) in order to raise model robustness. Among the machine learning classifiers used to further process acquired features in order to improve classification performance are Random Forest and XGBoost. Included to ensure model interpretability and enable clinicians to comprehend the decision-making process are explainable artificial intelligence (XAI) methods include Grad-CAM, SHAP, and LIME. The technology is applied as a real-time online tool allowing medical professionals to enter images and obtain immediately AI-driven diagnosis recommendations. Extensive experiments using publicly available liver imaging datasets are conducted in which the proposed model exhibits excellent accuracy, precision, recall, and F1-score for liver disease classification and segmentation tasks. Combining U-Net for segmentation with ResNet-50 for classification, this effort aims to offer a powerful, automated, and interpretable diagnostic tool for liver disease diagnosis thereby facilitating early intervention and better clinical decision-making.