Identifying Chaos in Skin Lesions Using Deep Learning : A potential examination tool for dermatologists

Abstract: This thesis investigated whether a deep learning model could learn features of Chaos,from the Chaos & Clues evaluation protocol, in a given dermatoscopic image data set. Asuccessful result could be of use in a future decision-support system for when dermatologists examine skin lesions for traces of melanoma (type of skin cancer). The chosen deep learning model (Inception V3) was trained to recognise four classesrelated to Chaos. Anonymous patient data was used, provided by the partnering companyGnosco. The data was partitioned into one or two classes depending on the symmetryproperties found in the corresponding image annotation. More than twenty differentmodel configurations was run to obtain the results in this thesis. The results indicate that the chosen model was not capable of learning features of Chaosfrom the dermatoscopical image data-set. Training the model to recognise features ofChaos resulted in an overfit system with low validation accuracy (close to 30%). The prediction target was changed to contrast the negative results from the Chaos classification. The chosen model was therefore configured to learn two classes, ’melanoma’ and’nevus’. This prediction target yielded a more positive result as the validation accuracywas close to 85%. However, the corresponding confusion matrix showed that these resultsare not trustworthy. It is inconclusive whether the negative results from the Chaos classification stem from thechosen approach or if the data set was insufficient for the task-difficulty. We propose adjustments to the data set for future work which could disclose if the outlined approach isviable or not.