Automated Classification of Liver Disease Stages and Tumor Detection using Hybrid Deep Learning Techniques

Deep Learning (DL), a distinguished branch of Artificial Intelligence and Machine Learning, has emerged as a transformative method for solving complicated troubles throughout numerous domains, specifically in medical imaging. Its capability to automatically analyze hierarchical representations from huge-scale facts makes it distinctly effective for sickness prognosis and class. This venture provides a hybrid DL model that mixes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to automate the classification of liver disease levels and hit upon tumors with advanced accuracy. CNNs are used to extract spatial capabilities from CT and MRI scans, efficaciously capturing the structural traits of liver tissues and abnormalities. LSTM networks, then again, are capable of studying temporal dependencies from sequences of photo slices, allowing the version to investigate ailment progression across multiple imaging stages. The integration of CNN and LSTM allows the model to apprehend both spatial and temporal factors of liver diseases, which substantially complements diagnostic overall performance. This hybrid architecture is specially suitable for multiphasic CT and MRI datasets, wherein temporal statistics performs a critical role. The proposed device gives a strong and dependable answer for helping clinicians in early detection, particular staging, and remedy planning of liver issues, contributing to higher affected person effects and healthcare performance.