# Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection

## TL;DR

This paper introduces a deep learning model combining CNNs, RNNs, and XAI to improve medical image retrieval for breast cancer detection.

## Contribution

The novelty lies in combining CNNs, RNNs, and XAI for high-accuracy medical image classification and retrieval.

## Key findings

- The model achieves 99.24% classification accuracy using the BUSI dataset.
- It performs effectively in retrieving relevant medical images for breast cancer detection.

## 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.