TY - JOUR
T1 - Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures
AU - Almaraz-Damian, Jose Agustin
AU - Ponomaryov, Volodymyr
AU - Sadovnychiy, Sergiy
AU - Castillejos-Fernandez, Heydy
N1 - Publisher Copyright:
© 2020 by the authors.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - In this paper, a newComputer-AidedDetection (CAD) systemfor the detection and classification of dangerous skin lesions (melanoma type) is presented, through a fusion of handcraft features related to the medical algorithm ABCD rule (Asymmetry Borders-Colors-Dermatoscopic Structures) and deep learning features employing Mutual Information (MI) measurements. The steps of a CAD system can be summarized as preprocessing, feature extraction, feature fusion, and classification. During the preprocessing step, a lesion image is enhanced, filtered, and segmented, with the aim to obtain the Region of Interest (ROI); in the next step, the feature extraction is performed. Handcraft features such as shape, color, and texture are used as the representation of the ABCD rule, and deep learning features are extracted using a Convolutional Neural Network (CNN) architecture, which is pre-trained on Imagenet (an ILSVRC Imagenet task). MI measurement is used as a fusion rule, gathering the most important information from both types of features. Finally, at the Classification step, several methods are employed such as Linear Regression (LR), Support Vector Machines (SVMs), and Relevant Vector Machines (RVMs). The designed framework was tested using the ISIC 2018 public dataset. The proposed framework appears to demonstrate an improved performance in comparison with other state-of-the-art methods in terms of the accuracy, specificity, and sensibility obtained in the training and test stages. Additionally, we propose and justify a novel procedure that should be used in adjusting the evaluation metrics for imbalanced datasets that are common for different kinds of skin lesions.
AB - In this paper, a newComputer-AidedDetection (CAD) systemfor the detection and classification of dangerous skin lesions (melanoma type) is presented, through a fusion of handcraft features related to the medical algorithm ABCD rule (Asymmetry Borders-Colors-Dermatoscopic Structures) and deep learning features employing Mutual Information (MI) measurements. The steps of a CAD system can be summarized as preprocessing, feature extraction, feature fusion, and classification. During the preprocessing step, a lesion image is enhanced, filtered, and segmented, with the aim to obtain the Region of Interest (ROI); in the next step, the feature extraction is performed. Handcraft features such as shape, color, and texture are used as the representation of the ABCD rule, and deep learning features are extracted using a Convolutional Neural Network (CNN) architecture, which is pre-trained on Imagenet (an ILSVRC Imagenet task). MI measurement is used as a fusion rule, gathering the most important information from both types of features. Finally, at the Classification step, several methods are employed such as Linear Regression (LR), Support Vector Machines (SVMs), and Relevant Vector Machines (RVMs). The designed framework was tested using the ISIC 2018 public dataset. The proposed framework appears to demonstrate an improved performance in comparison with other state-of-the-art methods in terms of the accuracy, specificity, and sensibility obtained in the training and test stages. Additionally, we propose and justify a novel procedure that should be used in adjusting the evaluation metrics for imbalanced datasets that are common for different kinds of skin lesions.
KW - Balance
KW - Computer-aided systems
KW - Convolutional neural networks
KW - Data
KW - Deep learning
KW - Fusion
KW - Handcraft
KW - Melanoma
KW - Mutual information
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85085093288&partnerID=8YFLogxK
U2 - 10.3390/E22040484
DO - 10.3390/E22040484
M3 - Artículo
SN - 1099-4300
VL - 22
JO - Entropy
JF - Entropy
IS - 4
M1 - 484
ER -