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Pixels to Classes: Intelligent Learning Framework for Multiclass Skin Lesion Localization and Classification

Khan, Muhammad Attique ; Zhang, Yu-Dong ; Sharif, Muhammad ; Akram, Tallha

Computers & electrical engineering, 2021-03, Vol.90, p.106956, Article 106956 [Periódico revisado por pares]

Amsterdam: Elsevier Ltd

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  • Título:
    Pixels to Classes: Intelligent Learning Framework for Multiclass Skin Lesion Localization and Classification
  • Autor: Khan, Muhammad Attique ; Zhang, Yu-Dong ; Sharif, Muhammad ; Akram, Tallha
  • Assuntos: Artificial neural networks ; Classification ; CNN Architecture ; Computer architecture ; Datasets ; Lesion Segmentation ; Machine learning ; Neural networks ; Parameter sensitivity ; Recognition ; Segmentation ; Skin Cancer
  • É parte de: Computers & electrical engineering, 2021-03, Vol.90, p.106956, Article 106956
  • Descrição: •Modified Mask RCNN based skin lesion segmentation is performed.•24-Layered CNN architecture is implemented.•Fully Connected layer is utilized for features mapping•Softmax classifier is employed for lesion type recognition A novel deep learning framework is proposed for lesion segmentation and classification. The proposed technique incorporates two primary phases. For lesion segmentation, Mask recurrent convolutional neural network (MASK R-CNN) based architecture is implemented. In this architecture, Resnet50 along with feature pyramid network (FPN) is utilized as a backbone. Later, fully connected layer-based features are mapped for the final mask generation. In the classification phase, 24-layered convolutional neural network architecture is designed, which performs activation based on the visualization of higher features. Finally, best CNN features are provided to softmax classifiers for final classification. Three datasets (i.e. PH2, ISBI2016, and ISIC2017) are utilized for the validation of the segmentation process, whilst HAM10000 dataset is utilized for the classification. From the results, it is concluded that the proposed method outperforms several existing techniques, based on the selected set of parameters including sensitivity (85.57%), precision (87.01%), F1- Score (86.28%), and accuracy (86.5%).
  • Editor: Amsterdam: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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