RT Journal Article
T1 An ensemble-based convolutional neural network model powered by a genetic algorithm for melanoma diagnosis
A1 Perez, Eduardo
A1 Ventura, Sebastian
K1 Convolutional neural networks
K1 Melanoma diagnosis
K1 Ensemble learning
K1 Genetic algorithm
K1 Lesion segmentation
K1 Skin-lesion segmentation
K1 Rejective multiple test
K1 Deep
K1 Classification
K1 Images
K1 Cnn
K1 Tests
K1 Big
AB Melanoma is one of the main causes of cancer-related deaths. The development of new computational methods as an important tool for assisting doctors can lead to early diagnosis and effectively reduce mortality. In this work, we propose a convolutional neural network architecture for melanoma diagnosis inspired by ensemble learning and genetic algorithms. The architecture is designed by a genetic algorithm that finds optimal members of the ensemble. Additionally, the abstract features of all models are merged and, as a result, additional prediction capabilities are obtained. The diagnosis is achieved by combining all individual predictions. In this manner, the training process is implicitly regularized, showing better convergence, mitigating the overfitting of the model, and improving the generalization performance. The aim is to find the models that best contribute to the ensemble. The proposed approach also leverages data augmentation, transfer learning, and a segmentation algorithm. The segmentation can be performed without training and with a central processing unit, thus avoiding a significant amount of computational power, while maintaining its competitive performance. To evaluate the proposal, an extensive experimental study was conducted on sixteen skin image datasets, where state-of-the-art models were significantly outperformed. This study corroborated that genetic algorithms can be employed to effectively find suitable architectures for the diagnosis of melanoma, achieving in overall 11% and 13% better prediction performances compared to the closest model in dermoscopic and non-dermoscopic images, respectively. Finally, the proposal was implemented in a web application in order to assist dermatologists and it can be consulted at .
PB Springer london ltd
SN 0941-0643
YR 2021
FD 2021-11-19
LK https://hdl.handle.net/10668/28193
UL https://hdl.handle.net/10668/28193
LA en
DS RISalud
RD Apr 18, 2025