Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection.
Authors
Fawzy AE,Almandouh ME,Herajy M,Eisa M
Affiliations (6)
Affiliations (6)
- Information Technology Management Department, Faculty of Management Technology and Information Systems, Port Said University, Port Said, Egypt. [email protected].
- Mathematics and Computer Science Department, Faculty of Science, Port Said University, Port Said, Egypt. [email protected].
- Information Technology Management Department, Faculty of Management Technology and Information Systems, Port Said University, Port Said, Egypt.
- Badr University in Cairo (BUC), Cairo11829, Badr, Egypt.
- Mathematics and Computer Science Department, Faculty of Science, Port Said University, Port Said, Egypt.
- Uruk University, Baghdad, Iraq.
Abstract
As digital imaging in healthcare grows quickly, dealing with vast medical image data is getting trickier. Content-Based Medical Image Retrieval (CBMIR) systems help with this, but they struggle because of the gap between simple image details and what these images mean in a clinical setting. This paper presents a new approach using deep learning for CBMIR that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Explainable AI (XAI). Using the Breast Ultrasound Image (BUSI) dataset for training, this hybrid model classifies images and finds the relevant results based on predictions. It reaches a classification accuracy of 99.24% and performs well in retrieval tasks.
Topics
Breast NeoplasmsDeep LearningImage Processing, Computer-AssistedJournal Article