Assessment of Segmentation Impact on Melanoma Classification Using Convolutional Neural Networks

Qikang Deng; Jose Cruz Castelo Beltran; DoHoon Lee

doi:10.5626/JCSE.2021.15.3.115

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저널정보

Korean Institute of Information Scientists and Engineers
Journal of Computing Science and Engineering 학술저널
Journal of Computing Science and Engineering Vol.15 No.3
2021.09 115 - 124 (10page)
DOI : 10.5626/JCSE.2021.15.3.115

저자정보

Qikang Deng (Pusan National University)
Jose Cruz Castelo Beltran (Pusan National University)
DoHoon Lee (Pusan National University)

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Among the different types of skin cancer, melanoma is the one with the highest death rate. Therefore, the early detection of melanoma and the development of technologies that can assist in this task have become significantly important. Convolutional neural networks are one of the most popular skin cancer classification methods. However, most of the available skin cancer datasets include images with lesions that are hard to differentiate from healthy skin or with a high presence of hair that can occlude the lesion. This characteristics of the images makes it harder to extract lesion features. Therefore, utilizing segmentation to extract the lesion location is an important step to reduce hair noise and improve lesion analysis. In this paper, two combining methods for segmentation and classification were explored: concatenation and multiplication. By utilizing these methods, it was possible to improve the accuracy of different neural network architectures by around 1% when compared to unmodified models without segmentation. The best-performing model was selected for further training. This model in conjunction with the segmentation module allowed for the correct re-classification of around 10% of the total examples in the dataset, indicating that a segmentation phase leads to an overall accuracy improvement and suggested that by improving the segmentation, an improvement on the overall accuracy can be obtained.

#Skin cancer #Melanoma classification #Medical image segmentation