# Efficient Medical Image Retrieval Using DenseNet and FAISS for BIRADS Classification

arXiv: https://arxiv.org/abs/2411.01473 · HTML: https://mlsanity.com/paper/2411.01473/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 3 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)
- [CNN-Based Projected Gradient Descent for Consistent Image Reconstruction](https://mlsanity.com/paper/1709.01809/) (2017)
- [Medical Image Retrieval using Deep Convolutional Neural Network](https://mlsanity.com/paper/1703.08472/) (2017)
- [Semi-supervised learning for medical image classification using imbalanced training data](https://mlsanity.com/paper/2108.08956/) (2021)
- [Deep Triplet Hashing Network for Case-based Medical Image Retrieval](https://mlsanity.com/paper/2101.12346/) (2021)
- [A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers](https://mlsanity.com/paper/2003.11154/) (2020)

## Built on by (most cited first, 3 of 3)

- [Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features](https://mlsanity.com/paper/2412.01555/) (2024)
- [Back Home: A Computer Vision Solution to Seashell Identification for Ecological Restoration](https://mlsanity.com/paper/2501.04873/) (2025)
- [Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization](https://mlsanity.com/paper/2508.04790/) (2025)

## Abstract (arXiv)

That datasets that are used in todays research are especially vast in the medical field. Different types of medical images such as X-rays, MRI, CT scan etc. take up large amounts of space. This volume of data introduces challenges like accessing and retrieving specific images due to the size of the database. An efficient image retrieval system is essential as the database continues to grow to save time and resources. In this paper, we propose an approach to medical image retrieval using DenseNet for feature extraction and use FAISS for similarity search. DenseNet is well-suited for feature extraction in complex medical images and FAISS enables efficient handling of high-dimensional data in large-scale datasets. Unlike existing methods focused solely on classification accuracy, our method prioritizes both retrieval speed and diagnostic relevance, addressing a critical gap in real-time case comparison for radiologists. We applied the classification of breast cancer images using the BIRADS system. We utilized DenseNet's powerful feature representation and FAISSs efficient indexing capabilities to achieve high precision and recall in retrieving relevant images for diagnosis. We experimented on a dataset of 2006 images from the Categorized Digital Database for Low Energy and Subtracted Contrast Enhanced Spectral Mammography (CDD-CESM) images available on The Cancer Imaging Archive (TCIA). Our method outperforms conventional retrieval techniques, achieving a precision of 80% at k=5 for BIRADS classification. The dataset includes annotated CESM images and medical reports, providing a comprehensive foundation for our research.