# Anatomy-specific classification of medical images using deep convolutional nets

arXiv: https://arxiv.org/abs/1504.04003 · HTML: https://mlsanity.com/paper/1504.04003/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2015.

## Built on by (most cited first, 14 of 14)

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- [INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses](https://mlsanity.com/paper/2309.02147/) (2023)
- [Fast CT anatomic localization algorithm](https://mlsanity.com/paper/2312.02941/) (2023)
- [EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion](https://mlsanity.com/paper/2505.17367/) (2025)

## Abstract (arXiv)

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.