# A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images

arXiv: https://arxiv.org/abs/2106.14207 · HTML: https://mlsanity.com/paper/2106.14207/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 77 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)
- [Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization](https://mlsanity.com/paper/2007.14895/) (2020)
- [Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray](https://mlsanity.com/paper/2004.06578/) (2020)
- [Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques](https://mlsanity.com/paper/2005.03357/) (2020)
- [Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images](https://mlsanity.com/paper/2012.02238/) (2020)

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

- [Is plantar thermography a valid digital biomarker for characterising diabetic foot ulceration risk?](https://mlsanity.com/paper/2407.04676/) (2024)
- [ConMatFormer: A Multi-attention and Transformer Integrated ConvNext based Deep Learning Model for Enhanced Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2510.22743/) (2025)
- [Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework](https://mlsanity.com/paper/2605.04079/) (2026)

## Abstract (arXiv)

Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ~95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97 %. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.