A Comprehensive Survey of AI-Driven Biomedical Image Processing for Intracerebral Hemorrhage Detection and Classification: Current Trends, Challenges, and Future Directions

Amongst many life-threatening conditions, a fatal condition is the Intracerebral hemorrhage (ICH). It may result in death if not treated immediately and hence necessitates the need for accurate diagnosis for effective treatment. In recent times, concepts of Artificial intelligence (AI), have emerged as a transformative tool in medical image processing, offering faster and more reliable ICH detection and classification. This research study reviews the current advancements in AI-driven biomedical image processing for ICH, exploring various state-of-the-art models involving Convolutional Neural Networks (CNNs), variants of CNN with more efficient feature extraction procedures, Long Short-Term Memory (LSTM) networks, and optimization methods like Genetic Algorithms (GA), Ant Colony Model (ACO), rParticle Swarm Optimization (PSO). Additionally, challenges such as data scarcity, model interpretability, and generalization across diverse populations are discussed. The paper highlights future research directions, including the need for explainable AI, multimodal data integration, and real-time, low-cost AI solutions for resourceconstrained environments. By addressing these challenges, AI has the potential to revolutionize ICH detection and improve patient outcomes globally.