Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma

Prasitthichai Naronglerdrit, Iosif Mporas

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

6 Downloads (Pure)

Abstract

This chapter presents a methodology for diagnosis of pigmented skin lesions using convolutional neural networks. The architecture is based on convolutional neural networks and it is evaluated using new CNN models as well as retrained modification of pre-existing CNN models were used. The experimental results showed that CNN models pre-trained on big datasets for general purpose image classification when re-trained in order to identify skin lesion types offer more accurate results when compared to convolutional neural network models trained explicitly from the dermatoscopic images. The best performance was achieved by retraining a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%. Analysis on skin lesion pathology type was also performed with classification accuracy for melanoma and basal cell carcinoma being equal to 79.13% and 82.88%, respectively.
Original languageEnglish
Title of host publicationDeep Learning for Cancer Diagnosis, Series Volume: 908, DOI: , eBook ISBN:
Subtitle of host publicationStudies in Computational Intelligence
Pages79-98
Volume908
ISBN (Electronic)978-981-15-6321-8
DOIs
Publication statusPublished - 1 Jan 2021

Fingerprint

Dive into the research topics of 'Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma'. Together they form a unique fingerprint.

Cite this