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Image Visibility Filter-Based Interpretable Deep Learning Framework for Skin Lesion Diagnosis
- Source :
- IEEE Transactions on Industrial Informatics. 18:5138-5147
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Computer aided diagnosis have made a significant breakthrough in skin lesion diagnosis employing deep learning (DL) frameworks over the years, but it hardly reveals the transparency of the DL architecture. To mitigate this issue, this article proposes an image visibility filter (IVF) based DL framework for skin lesion diagnosis. The proposed IVF-DL network employs a ResNet architecture where visibility patches, extracted from the image visibility graph (IVG), are used as the convolutional kernels to extract salient features from dermoscopic images. The primary aim of this article is not only to classify skin lesions but also to depict the interpretable results after each residual block in a supervised manner. An optimal performance has been obtained by tuning three hyperparameters of the proposed method. Furthermore, the final interpretable result has been analyzed via IVG to resemble its spatial characteristics. Experimental results reveal the efficacy of the proposed method both qualitatively and quantitatively.
- Subjects :
- Hyperparameter
Computer science
business.industry
Visibility graph
Deep learning
Visibility (geometry)
Pattern recognition
Filter (signal processing)
Computer Science Applications
Control and Systems Engineering
Computer-aided diagnosis
Transparency (data compression)
Artificial intelligence
Electrical and Electronic Engineering
business
Information Systems
Block (data storage)
Subjects
Details
- ISSN :
- 19410050 and 15513203
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Industrial Informatics
- Accession number :
- edsair.doi...........a7276bd5b5b57c7b627710b75491c06a