Body map based wound image classification using deep learning
Date
2023-05
Authors
Khanal, Bibek
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
Abstract
Identifying different types of ulcer and surgical wounds based on their distinct
features is a complex task in medical imaging. This involves the classification of
ulcer and surgical wound into various labels such as diabetic ulcer, pressure ulcer,
venous ulcer and surgical wounds. In order to make this process more efficient
and cost-effective, there has been different study in this field. A body map based
VGG 16 network is used to implement transfer learning onto two trainable dense
layers for classification of wound images into five labels. The five labels include
the aforementioned four types of wound and another label "Not a wound" which
does not contain any wound image. The study is started with AZHMT dataset
containing 4790 images. These images are classified using pre-trained inceptionV3
and VGG 16 network separately. The performance of VGG 16 was found to be
better than inceptionV3 by almost 4% which was the reason for selecting VGG
16 for further study in this dataset. Also, inceptionV3 is longer and wider than
VGG 16 which will learn unnecessary features from images using higher computing
resources. The main aim of this thesis is to show that performance can be increased
without learning unnecessary features, using fewer computing resources. and by
using body map function.
Description
Identifying different types of ulcer and surgical wounds based on their distinct
features is a complex task in medical imaging. This involves the classification of
ulcer and surgical wound into various labels such as diabetic ulcer, pressure ulcer,
venous ulcer and surgical wounds.
Keywords
Transfer Learning,, Body Map,, Surgical Wounds,