Increasing Skin Lesions Classification Rates using Convolutional Neural Networks with Invariant Dataset Augmentation and the Three-Point Checklist of Dermoscopy
Ładowanie...
Data
2023
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
In the paper, we show how to tackle the problem of lack of the
rotation invariance in CNN networks using the authors’ Invariant Dataset
Augmentation (IDA) method. The IDA method allows to increase the classification
rates taking into account as an example the classification of the
skin lesions using a small image set. In order to solve the problem of the
lack of rotation invariance, IDA method was used and the dataset was increased
in an eightfold and invariant way. In the research, we applied the
IDA methods and compared the results of VGG19, XN and Inception-ResNetv2
CNN networks in three skin lesions features classification defined by wellknown
dermoscopic criterions e.g. the Three-Point Checklist of Dermoscopy
or the Seven-Point Checklist. Due to Invariant Dataset Augmentation, the
classification rate parameters like true positive rate by almost 20%, false
positive rate as well as the F1 score and Matthews correlation coefficient
have been significantly increased opposite to type II error that has significantly
decreased. In the paper, the confusion matrix parameters result in:
98-100% accuracy, 98-100% true positive rate, 0.0-2.3% false positive rate,
tests F1=0.95 and MCC=0.95. That general approach can provide higher
results while using CNN networks in other applications.
Opis
Słowa kluczowe
invariant dataset augmentation, dermoscopic images, blue-white veil, lesion symmetry, convolutional neural networks, artificial intelligence, inwariantne powiększanie zbioru danych, obrazy dermoskopowe, niebiesko-biała zasłona, symetria zmian, splotowe sieci neuronowe, sztuczna inteligencja
Cytowanie
Milczarski P., Borowski N., Beczkowski M., Increasing Skin Lesions Classification Rates using Convolutional Neural Networks with Invariant Dataset Augmentation and the Three-Point Checklist of Dermoscopy. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 325-334, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.52.