Deep learning for melanoma classification

: a study using skin lesion images

Abstract

Cutaneous melanoma is considered the skin cancer with highest mortality rate and has been gaining the attention of the medical community due to its rapidly increasing incidence. Advancements in computational technologies have paved the way for innovative image detection methods that can be transferable to medical applications, significantly enhancing the potential for early intervention in melanoma diagnosis. To make diagnosis more accurate and to further increase survival rates,this study employs deep learning techniques on an extensive dataset derived from multiple sources. Utilizing Microsoft Azure Cloud as the computational infrastructure, trial and error approach was employed by hyper parameterizing several convolutional neural networks (CNN) where the decision criteria were choosing the one with highest Fβ Score. MAR-MELA-CNN is an innovative ensemble model in corporating six state-of-the-art pre-trained CNN architectures: Xception, VGG16, ResNet50, NASNetMobile, MobileNetV2, and InceptionV3. The primary goal ofthis research is to further understand CNN’s efficiency in the diagnosis of melanoma and to furthermore measure its performance on a merged dataset. The proposed algorithm achieved a Fβ score of 85%, an area under the curve (AUC) score of 93%, and an average precision (AP) score of 92%, promising diagnostic tool for cutaneous melanoma compared to traditional methods. Further improvements lay in the improvement of the architecture, expansion of the computational instances as well as of the dataset. Another field of future work could be devising a strategy for real-time implementation of this model in a hospital setting, as it could be of vital importance to provide swift support to doctors.

Date of Award	5 Jul 2023
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Pedro Afonso Fernandes (Supervisor)