This paper tackles the challenge of Feature-Based Medical Image Retrieval (FBMIR) by proposing a novel deep learning framework built upon a deep convolutional neural network (CNN). Our approach addresses the key aspects of achieving high retrieval performance in FBMIR: learning effective feature representations and designing robust similarity metrics. We argue that these two elements play a crucial role in accurately retrieving relevant medical images, and our framework demonstrates significant advancement in both. The critical role of digital imaging in hospitals and clinics has fueled the rapid growth and demand for extensive medical image databases. As digital imaging becomes ubiquitous in healthcare, medical image databases are expanding at an unprecedented pace to meet the needs of hospitals and clinics. Large medical image databases and the complexities of querying them motivate the development of Feature-Based Medical Image Retrieval (FBMIR) frameworks. A major challenge in Content-Based Image Retrieval (CBIR) is the “semantic gap” between low-level visual features and high-level concepts. Machine learning, particularly deep learning, offers promising solutions to bridge this gap. Inspired by advancements in deep learning, we propose a framework leveraging its capabilities to learn effective feature representations and robust similarity metrics for FBMIR tasks. Our experiments demonstrate significant improvements in retrieval performance, highlighting the potential of deep learning in FBMIR. Furthermore, we identify key findings and promising directions for future research.
Implementation of Feature-Based Medical Image Retrieval (FBMIR) Through a Deep Convolutional Neural Network (CNN)
Smart Innovation, Systems and Technologies
Congress on Control, Robotics, and Mechatronics ; 2024 ; Warangal, India February 03, 2024 - February 04, 2024
Proceedings of the Second Congress on Control, Robotics, and Mechatronics ; Chapter : 26 ; 333-348
2024-10-31
16 pages
Article/Chapter (Book)
Electronic Resource
English
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