A Machine Learning–Based Framework for Medical Image Classification in Early Chronic Disease Detection

One of the essential points of computer-aided diagnosis is medical image classification which enables timely and accurate diagnosis of diseases.The fast development of medical imaging systems including ultrasound, X-ray, CT, and MRI has put more pressure on the need to have automated and reliable diagnostic systems. The traditional analysis of images are usually based on manual interpretation and handcrafted features that can be a limiting factor in scalability and consistency in clinical practice. To circumvent these shortcomings, this paper introduces a machine-learned framework of automated medical image classification that would help in assisting the early detection of diseases. Models of Deep learning, mainly Convolutional Neural Networks (CNNs) are also used to discover discriminatory features in the spatial domain in high-dimensional medical images automatically, without requiring manual feature engineering. The methodological design and system architecture were highlighted in this study is appropriate for development of intelligent diagnostic systems. The suggested framework is supposed to enhance the efficiency of diagnostics, decrease the flight of clinicians, and become a base of further extensions with explainable artificial intelligence and clinical decision support systems. In general, this paper leads to the creation of scalable and reliable AI-based medical image classification assistants to healthcare applications.