An ensemble-based convolutional neural network model powered by a genetic algorithm for melanoma diagnosis

Abstract

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 http://skinensemble.com.

Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

“…Initially, we chose the pretrained models from the previous research, as in [26,52]. The best three methods were selected after comparing their accuracy to other methods using the previously mentioned free dataset of chest X-ray images.…”

Recent advances on effective and efficient deep learning-based solutions

“…Pérez and Ventura [ 14 ] proposed 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.…”

“…The rest of the papers have been studied the target characteristics in different application domains: Natural Language Processing (5 papers): Ali et al [ 2 ] (recommender systems, citation recommendations, network embedding & sparsity), Ji et al [ 7 ] (suicidal ideation, mental disorder, attentive relation networks), Pandelea et al [ 12 ] (retrieval-based dialogue system, limited resourcees, dual-encoder architecture), Paul et al [ 13 ] (ensemble learning, support vector machine, music symbol recognition), Zhang et al [ 20 ] (neural summarisation methods, content selection, information fusion, reinforcement learning). Health (4 papers): Amor et al [ 3 ] (breast cancer, DNA methylation, deep embedded refined clustering), Nogueira-Rodríguez et al [ 11 ] (colorectal cancer, polyp detection, YOLOv3 architecture), Pérez and Ventura [ 14 ] (melanoma diagnosis, lesion segmentation, ensemble learning, genetic algorithm); Qureshi et al [ 15 ] (cardiovascular, healthcare systems, sensors, wearable technologies). Image and audio processing (6 papers): Fenza et al [ 5 ] (graph neural networks, name–face association, multimedia content), Huertas-Tato et al [ 6 ] (multi-view image, solar irradiance, Total Sky Images), Tarasiuk and Szczepaniak [ 18 ] (geometric transformations, invariance to rotation and scale CNN), Rodriguez-Conde et al [ 16 ] (object detection, on-device machine learning), Leroux et al [ 8 ] (storage requirements, residual networks, adaptive computation, resource-constrained deep learning), Li et al [ 9 ] (discrimination, Softmax loss, features discrimination, margin constraints).…”

“…Health (4 papers): Amor et al [ 3 ] (breast cancer, DNA methylation, deep embedded refined clustering), Nogueira-Rodríguez et al [ 11 ] (colorectal cancer, polyp detection, YOLOv3 architecture), Pérez and Ventura [ 14 ] (melanoma diagnosis, lesion segmentation, ensemble learning, genetic algorithm); Qureshi et al [ 15 ] (cardiovascular, healthcare systems, sensors, wearable technologies). Image and audio processing (6 papers): Fenza et al [ 5 ] (graph neural networks, name–face association, multimedia content), Huertas-Tato et al [ 6 ] (multi-view image, solar irradiance, Total Sky Images), Tarasiuk and Szczepaniak [ 18 ] (geometric transformations, invariance to rotation and scale CNN), Rodriguez-Conde et al [ 16 ] (object detection, on-device machine learning), Leroux et al [ 8 ] (storage requirements, residual networks, adaptive computation, resource-constrained deep learning), Li et al [ 9 ] (discrimination, Softmax loss, features discrimination, margin constraints).…”

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

“…A deep convolutional neural network was developed in [19] for multi-class skin Cancer classification. However, deep learning computer-aided schemes were not applied.…”