An integrated design for segmentation and classification of diabetic foot ulcers using thermography images

TL;DR

This paper introduces a new system using thermography and AI to accurately detect and classify diabetic foot ulcers at an early stage.

Contribution

A novel intelligent prediction system combining modified swin transformers and deep capsule networks for DFU segmentation and classification.

Findings

The proposed system achieves 99% prediction accuracy and 98.6% segmentation performance.

The model outperforms existing methods in early detection of diabetic foot ulcers.

Abstract

In recent days, diabetics, a chronic disease has risen significantly which leads to more health complications. Among those complications, diabetic foot ulcer (DFU) is much serious. DFU is a wound on the foot of a person who is affected with diabetics. It sometimes leads to fatality if untreated. Diagnosing the DFU in its early stage remains challenging due to medical impediments by the diabetics. Thermography serves as a promising technique in the early prediction of the DFU and aids for an improvised treatment towards the eradication of foot amputations. But still, utilizing thermography images for clinical treatments continues to be underexplored in treating DFU due to its computational complexities and existence of ambiguities in thermal images. To overcome this challenge, this research paper proposes an Intelligent Prediction System (IPS) using the modified swin transformers for an effective segmentation and deep capsule networks for an accurate prediction of DFU. In the segmentation phase, swin transformers can be used as U-NET based architecture to segment the lesions of foot ulcers. Deep features are extracted by the capsule networks and supplied to the deep shallow network which works on the standard of extreme learning networks to achieve the early prediction of DFU. The extensive experimentation is conducted using the thermal foot ulcer images in Python3.20 and Tensorflow –Keras Libraries. To verify the efficiency of the proposed schema, evaluated performances are assessed with other research experiments. Results show that the proposed schema achieves the highest prediction accuracy (99%) with promising segmented performance (98.6%). Moreover, the proposed model excels the varied residing schema and establishes a firm foothold in the early prediction of DFUs.

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