Scar detection using deep neural networks

Sammanfattning: Object detection is a computer vision method that deals with the tasks of localizing and classifying objects within an image. The number of usages for the method is constantly growing, and this thesis investigates the unexplored area of using deep neural networks for scar detection. Furthermore, the thesis investigates using the scar detector as a basis for the binary classification task of deciding whether in-the-wild images contains a scar or not. Two pre-trained object detection models, Faster R-CNN and RetinaNet, were trained on 1830 manually labeled images using different hyperparameters. Faster R-CNN Inception ResNet V2 achieved the highest results in terms of Average Precision (AP), particularly at higher IoU thresholds, closely followed by Faster R-CNN ResNet50, and finally RetinaNet. The results both indicate the superiority of Faster R-CNN compared to RetinaNet, as well as using Inception ResNet V2 as feature extractor for a large variety of object sizes. The reason is most likely due to multiple convolutional filters of different sizes operating at the same levels in the Inception ResNet network. As for inference time, RetinaNet was the fastest, followed by Faster R-CNN ResNet50 and finally Faster R-CNN Inception ResNet V2. For the binary classification task, the models were tested on a set of 200 images, where half of the images contained clearly visible scars. Faster R-CNN ResNet50 achieved the highest accuracy, followed by Faster R-CNN Inception ResNet V2 and finally RetinaNet. While the accuracy of RetinaNet suffered mainly from a low recall, Faster R-CNN Inception ResNet V2 detected some actual scars in images that had not been labeled due to low image quality, which could be a matter of subjective labeling and that the model is punished for something that at other times might be considered correct. In conclusion, this thesis shows promising results of using object detection to detect scars in images. While two-stage Faster R-CNN holds the advantage in AP for scar detection, one-stage RetinaNet holds the advantage in speed. Suggestions for future work include eliminating biases by putting more effort into labeling data as well as including training data that contain objects for which the models produced false positives. Examples of this are wounds, knuckles, and possible background objects that are visually similar to scars.