Diabetic Foot Ulcer Measurement of Wound Model Shapes in clinical setting Using NN

Computer Science

The manual identification of detected things from the given data which is very time consuming. We propose an approach which uses neural network to identify the defects in the apple. Diabetic Foot Ulcer classification is a difficult challenge due to the numerous types of Diabetic Foot Ulcers. In order to recognize Diabetic Foot Ulcers more accurately, we proposed a hybrid classification method based on fitness-scaled chaotic artificial bee colony (FSCABC) algorithm and feedforward neural network (FNN). First, Diabetic Foot Ulcers images were acquired by a digital camera, and then the background of each image were removed by split-and-merge algorithm. We used a square window to capture the Diabetic Foot Ulcers, and download the square images to 256 256. Second, the color histogram, texture and shape features of each Diabetic Foot Ulcer image were extracted to compose a feature space. Third, principal component analysis was used to reduce the dimensions of the feature space. Finally, the reduced features were sent to the FNN, the weights/biases of which were trained by the FSCABC algorithm. We also used a stratified K-fold cross validation technique to enhance the generation ability of FNN. The experimental results of the 1653 color Diabetic Foot Ulcer images from the 18 categories demonstrated that the FSCABC–FNN achieved a classification accuracy of 89.1%. The classification accuracy was higher than Genetic Algorithm–FNN (GA–FNN) with 84.8%, Particle Swarm Optimization–FNN (PSO–FNN) with 87.9%, ABC–FNN with 85.4%, and kernel support vector machine with 88.2%. Therefore, the FSCABC–FNN was seen to be effective in classifying Diabetic Foot Ulcers.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.