Classification of ulcer images using convolutional neural networks
Year: 2018
Type: dissertation
Abstract: The use of artificial intelligence has increased within a variety of different fields the last decade, including the area of health care.Machine learning algorithms have already been successfully used in e.g.skin cancer detection in images, indicating its potential for being applied to wound treatment by automatically classifying wound images.In addition to reducing patient discomfort, a machine learning based solution for wound image classification could lower the costs for treatment of wounds.Today, this cost corresponds to the third biggest health care cost for Västra Götalandsregionen, the health care sector in Västra Götaland.This master thesis is a collaboration between the electronics and software development company QRTECH and Västra Götalandsregionen with the aim of creating an image-based aid to be used in the diagnosis of venous leg ulcers.A total of 300 manually annotated images depicting various ulcers for training a network have been provided by Alexandra Forssgren, specialist in dermatology and head of the dermatology department at Skaraborg Hospital.A deep learning based algorithm, using a pre-trained version of the VGG19 convolutional neural network, has been implemented to perform automated binary classification in order to separate venous leg ulcers from other ulcers.Since the annotated ulcer data set was limited, transfer learning was used by pre-training on ImageNet as well as on a set of dermoscopic skin images.The network was implemented in Python using the deep learning library Keras with TensorFlow as backend.To improve performance and reduce overfitting, methods such as dropout, L1/L2 regularisation and data augmentation were applied.Augmentation included rotation, flipping, contrast enhancement as well as added noise and modified brightness.The best set-up was selected based on the performance metrics accuracy, precision and recall.For the test set, the final network reached an accuracy, precision and recall of approximately 85 %, 82 % and 75 %, respectively.Most certainly, the limited size of the data set affected the results negatively and a larger data set would increase the variety of the training examples, possibly leading to better performance.However, the performance metrics are still promising considering the small data set which proves the potential of a machine learning based solution for ulcer image classification.
Authors: Charlotta Aguirre Nilsson, Medina Velic
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2889535768