Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Classification in Imbalanced Data Sets

• URL: http://arxiv.org/abs/2103.12068v2
• Date: Wed, 24 Mar 2021 15:05:48 GMT
• Title: Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Classification in Imbalanced Data Sets
• Authors: Aqsa Saeed Qureshi and Teemu Roos
• Abstract summary: Machine learning techniques for accurate classification of skin cancer from medical images have been reported. Many techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with patient information (meta-data) are combined using a meta-learner.
• Score: 0.6802401545890961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early diagnosis plays a key role in prevention and treatment of skin cancer.Several machine learning techniques for accurate classification of skin cancer from medical images have been reported. Many of these techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. However, the classification accuracy of these models still tends to be severely limited by the scarcity of representative images from malignant tumours. We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with patient information (meta-data) are combined using a meta-learner. The proposed approach improves the model's ability to handle scarce, imbalanced data. We demonstrate the benefits of the proposed technique using a dataset with 33126 dermoscopic images from 2000 patients.We evaluate the performance of the proposed technique in terms of the F1-measure, area under the ROC curve (AUC-ROC), and area under the PR curve (AUC-PR), and compare it with that of seven different benchmark methods, including two recent CNN-based techniques. The proposed technique achieves superior performance in terms of all the evaluation metrics (F1-measure $0.53$, AUC-PR $0.58$, AUC-ROC $0.97$).

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