Mini-DDSM: Mammography-based Automatic Age Estimation

TL;DR

This paper introduces Mini-DDSM, a new mammography dataset, and proposes an AI-based model using deep learning features and Random Forests to estimate age from mammogram images, achieving an average error of around 8 years.

Contribution

It is the first study to perform age estimation from mammograms, introducing a new dataset and a novel AI approach for this task.

Findings

Average age estimation error of about 8 years

Validation with logistic and linear regression models

Introduction of the free Mini-DDSM dataset

Abstract

Age estimation has attracted attention for its various medical applications. There are many studies on human age estimation from biomedical images. However, there is no research done on mammograms for age estimation, as far as we know. The purpose of this study is to devise an AI-based model for estimating age from mammogram images. Due to lack of public mammography data sets that have the age attribute, we resort to using a web crawler to download thumbnail mammographic images and their age fields from the public data set; the Digital Database for Screening Mammography. The original images in this data set unfortunately can only be retrieved by a software which is broken. Subsequently, we extracted deep learning features from the collected data set, by which we built a model using Random Forests regressor to estimate the age automatically. The performance assessment was measured using the mean absolute error values. The average error value out of 10 tests on random selection of samples was around 8 years. In this paper, we show the merits of this approach to fill up missing age values. We ran logistic and linear regression models on another independent data set to further validate the advantage of our proposed work. This paper also introduces the free-access Mini-DDSM data set.