AIM-Net: An Artificial Intelligence and Machine Learning Network for Advanced Medical Image Processing

چکیده

Medical image processing has become a critical component of modern healthcare systems, enabling accurate diagnosis, treatment planning, and disease monitoring. Recent advances in artificial intelligence and machine learning have significantly improved the performance of automated image analysis systems; however, challenges such as variability in imaging modalities, noise, and limited annotated datasets still hinder robust generalization. This paper introduces AIM-Net, an Artificial Intelligence and Machine Learning network designed for advanced medical image processing across multimodal datasets. The proposed framework integrates adaptive feature extraction, multiscale representation learning, and a hybrid deep learning architecture to enhance image quality, segmentation accuracy, and classification performance. AIM-Net employs a self-optimizing training strategy that combines supervised and semi-supervised learning to effectively leverage limited labeled data while improving model robustness. Extensive experiments conducted on benchmark medical imaging datasets demonstrate that AIM-Net outperforms existing state-of-the-art methods in terms of accuracy, sensitivity, and computational efficiency. The results highlight the effectiveness of the proposed approach in handling complex imaging variations and improving diagnostic reliability. This work contributes a scalable and generalizable framework for intelligent medical image processing and paves the way for future AI-driven clinical decision-support systems.