EARLY DETECTION AND CLASSIFICATION OF KNEE DISEASE USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES: AN OVERVIEW

Abstract

Early detection and accurate classification of knee-related diseases such as osteoarthritis, ligament injuries, and meniscus tears are critical for effective treatment planning and improved patient outcomes. With the rapid advancements in artificial intelligence, particularly in machine learning (ML) and deep learning (DL), automated diagnostic systems have emerged as promising tools in the field of medical imaging. This survey presents a comprehensive analysis of recent methodologies and frameworks employed in the early detection and classification of knee diseases using ML and DL approaches. The study explores traditional ML techniques, including support vector machines (SVM), random forests (RF), k-nearest neighbours (KNN), and ensemble methods, which rely heavily on handcrafted features and domain-specific knowledge. Furthermore, the review delves into the transformative impact of deep learning, especially convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures such as VGG, ResNet, and LSTM, which autonomously extract spatial and temporal features from raw medical images. This survey also covers key aspects such as image preprocessing, segmentation, feature extraction, and model evaluation using metrics like accuracy, precision, recall, F1-score, and AUC. Emphasis is placed on the role of data augmentation, normalization, and transfer learning in enhancing model performance. Additionally, the paper discusses publicly available datasets, challenges of class imbalance, interpretability of models, and computational efficiency. Comparative insights into 2D versus 3D image processing, integration of MRI and X-ray modalities, and recent trends in multimodal fusion are also addressed. The findings underscore the growing dominance of DL models, particularly hybrid frameworks that combine the strengths of multiple networks to deliver superior diagnostic accuracy. This survey aims to guide future research by identifying gaps, highlighting best practices, and providing a foundational understanding of intelligent systems for early knee disease detection.