Skin lesion classification using deep neural networks Skin cancer - PowerPoint PPT Presentation

Oct 23, 2023 •159 likes •276 views

Skin lesion classification using deep neural networks Skin cancer and effects >10000 cases of highly dangerous types of skin cancer in Sweden 2016 Of which roughly 4000 were malign melanoma Annual growth of 4.7% between 2006

Skin lesion classification using deep neural networks
Skin cancer and effects ● >10000 cases of highly dangerous types of skin cancer in Sweden 2016 ○ Of which roughly 4000 were malign melanoma ● Annual growth of 4.7% between 2006 and 2016 ○ Fastest growing type of cancer in the period
Task and ISIC2018 ● Dataset: HAM10000 ○ Created by Tschandl et al. From the department of dermatology at the medicinal University of Vienna ○ And Cliff Rosendahl from the faculty of medicine at the University of Queensland. ● The dataset was used in the competition: ISIC2018.
Dataset: 10k pictures of 7 lesions
Dataset: 10k pictures of 7 lesions
Data imbalance Dangerous lesions: ● akiec ● bcc ● mel
Convolutional neural networks Credit: F. Chollet ● Takes shape of picture into account ● Many layers can combine simple shapes into more advanced features
How we handled lack of data and data imbalance ● Small amount of data means risk of overfitting ● Imbalance causes a risk of the larger classes dominating classifications
Methods to deal with the problems ● Image augmentation ○ Only symmetrical flips improved performance ● Class weights in the loss function ● Transfer learning
Final result ● Our balanced accuracy: 64% ● Best ISIC2018 with the same data: 84% ● Best with similar approach: 76%
Future work ● Image segmentation (Cropping) ● Ensemble: Combining multiple classifiers. ● Try more image augmentation methods. Credit to: Domenico Daniele Bloisi

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