Deep ensemble learning for skin lesions classification with convolutional neural network

Renny Amalia Pratiwi, Siti Nurmaini, Dian Palupi Rini, Muhammad Naufal Rachmatullah, Annisa Darmawahyuni

Abstract


One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in cancer diagnosis, therefore the patient survival can be improved. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture not produce a satisfying performance. The DL approach allows the development of a medical image analysis system that can utilize an extraordinary network for improving the performance, such as the deep convolutional neural networks (DCNNs) method. In this study, we propose an ensemble learning approach that combines three DCNNs architectures such as Inception V3, Inception ResNet V2 and DenseNet 201 for improving the performance in terms of accuracy, sensitivity, specificity, precision, and F1-score. Seven classes of dermoscopy image categories of skin lesions is utilized with 10015 dermoscopy images from well known the HAM10000 dataset. As found in the results with proposed model ensemble learning architecture produce good classification performance with 97.23% accuracy, 90.12% sensitivity, 97.73% specificity, 82.01% precision, and 85.01% F1-Score. This method gives promising results in classifying skin lesions for cancer diagnosis. 

Keywords


Deep convolutional neural network; Ensemble learning; Melanoma; Skin lesion



DOI: http://doi.org/10.11591/ijai.v10.i3.pp%25p

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