Skin lesion classification from dermoscopic images using deep learning techniques
By Adria Romero Lopez, Xavier Giró-i-Nieto, Jack Burdick, Oge Marques. BioMed, 2017
354 citations recorded by the source corpus.
The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patients health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign. The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66%, which is significantly higher than the current state of the art on that dataset.
Subfields: Artificial Intelligence, Oncology
View Paper: https://www.semanticscholar.org/paper/5eb74dbee006337152b7760d90bb4668581c31ac
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In Artificial Intelligence
Citations in the corpus
- Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review (2018, methodology): “A modified VGGNet was also utilized by Lopez et al [31], where the classification of melanoma versus nevi or lentigines was addressed using dermatoscopic images.”
- A scoping review of transfer learning research on medical image analysis using ImageNet (2020, methodology): “, [70,91]) applied both feature extracting and fine-tuning TL approaches on the same task, and compared their performance.”
- Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval (2021, neutral): “analysis, with great results especially on the segmentation and the complete classification tasks [33], [36].”
- Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network (2022, methodology): “Visual Geometry Group (VGG) ConvNet architecture is used for transfer learning in this work [19].”