A REVIEW ON MEDICAL IMAGE CLASSIFICATION
Ms. Kanika Singhal Department of Computer Science Engineering
Inderprastha Engineering College Ghaziabad, India kanika.singhal@ipec.org.in
Dheeraj Jha
Department of Computer Science Engineering Inderprastha Engineering College
Ghaziabad, India 2103010062@ipec.org.in
Manish Chaudhary
Department of Computer Science Engineering
Inderprastha Engineering College Ghaziabad, India 2103010091@ipec.org.in
Abstract— Deep gaining knowledge (DL) has revolutionized medical image evaluation with the aid of improving diagnostic accuracy, efficiency, and patient care. This review synthesizes advancements, challenges, and future directions in making use of DL to medical imaging, drawing insights from comprehensive research and surveys. Deep getting to know architectures, mainly convolutional neural networks (CNNs), have confirmed super capabilities in diverse imaging duties, inclusive of dermatologist-degree pores and skin most cancers category and chest radiograph diagnosis, regularly matching or surpassing human knowledge. Those advancements underscore the transformative potential of integrating artificial intelligence (AI) into clinical workflows. but, challenges persist, along with facts shortage, moral issues, and the want for interpretable fashions to foster agreement with and adoption in scientific exercise. rising frameworks, along with self-explainable AI, cope with these obstacles by way of imparting transparency and interpretability in diagnostic choices. This paper additionally highlights the convergence of AI with high-performance medicinal drugs, advocating for multimodal facts integration and robust validation methodologies to further improve DL structures. By consolidating insights from diverse research, this assessment offers a roadmap for advancing DL technologies in clinical imaging, emphasizing the significance of innovation, collaboration, and addressing current boundaries to maximize scientific effect.
Keywords— Deep Learning. Medical Image Analysis, Artificial Intelligence, Convolutional Neural Networks (CNNs), Diagnostic Accuracy.
