A novel approach for skin lesion symmetry classification with a deep learning model

Talavera-Martínez, Lidia; Bibiloni, Pedro; Giacaman, A.; Ferrer, Rosa Taberner; Pozo, L.J. del; González-Hidalgo, Manuel

doi:10.1016/j.compbiomed.2022.105450

Cited by 20 publications

(6 citation statements)

References 22 publications

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“…Skin lesion classification. Skin lesion classification based on dermoscopic imagery remains a challenging problem due to the large inter-class similarity and intra-class variation [28,8,44]. For instance, ARL [57] proposes an attention residual module that uses high-level feature maps to generate attention maps for lower levels to allow the model paying more attention to semantically important regions.…”

Section: Medical Image Classificationmentioning

confidence: 99%

Class-Specific Distribution Alignment for semi-supervised medical image classification

Huang

Wang

et al. 2023

Computers in Biology and Medicine

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Section: Medical Image Classificationmentioning

confidence: 99%

Class-Specific Distribution Alignment for semi-supervised medical image classification

Huang

Wang

et al. 2023

Computers in Biology and Medicine

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“…11 The novel deep learning approaches can be seen in, 12 where the LCNet model obtained accuracy results of 0.8141, 0.8823, and 0.9042 on the International Skin Imaging Collaboration (ISIC) dataset's 2016, 2017, and 2020 instances. In 2022, a novel approach to skin lesion image classification was implemented by, 13 where the symmetry of skin lesions is considered, leading to the generation of a new dataset and a different method of melanoma image classification. In 2023, a novel Automated Deep Learning Melanoma Detection and Classification (ADL-MDC) technique, which includes preprocessing, K-means segmentation, feature extraction, and parameter optimization, obtained better results over recent deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Quantum computing meets skin cancer diagnosis

Lopez Montiel,

Montiel,

Lopez Montiel

et al. 2023

Optics and Photonics for Information Processing XVII

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Skin cancer is one of the most common and lethal diseases in America, and its early detection and treatment is the best approach to protect the lives of those afflicted. Computer-Aided Diagnosis systems have been implemented for decades as evidence of intelligent methods applied in the medical field. In recent years, machine and deep learning fields have shown great potential in medical diagnosis, particularly in skin cancer classification. These methods enable the automated extraction of complex input features, such as those in medical imagery. Therefore, with the advent of quantum computing, it is now possible to perform complex computations with increased speed and efficiency. This study explores the application of quantum machine learning in the classification of skin cancer using the ResNet50 model, a deep convolutional neural network for RGB images. The research employs a quantum-enhanced version of ResNet50 using a quanvolutional layer that the input skin lesion images go through and compare its performance with the classical ResNet50 version. We show comparative experiments, and the results indicate that further experiments are needed using more extensice datasets and different quantum deep learning architectures.

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“…Skin lesion mainly refers to a skin area with distinctive characteristics, such as color, shape, size, and texture, from the other surrounding areas of skin. The leading cause of this may be sunburn or contact dermatitis, which causes localized damage to the skin [ 1 , 2 , 3 ]. The American Society for Dermatologic Surgery describes a skin lesion as an abnormal lump, bump, ulcer, sore, or colored skin area.…”

Section: Introductionmentioning

confidence: 99%

Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

Mohanty

Pradhan

Reddy³

et al. 2022

Cancers

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This study mainly focuses on pre-processing the HAM10000 and BCN20000 skin lesion datasets to select important features that will drive for proper skin cancer classification. In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). Then, two other strategies, Model-based Optimized Weighted Feature Set (MOWFS) and Feature-based Optimized Weighted Feature Set (FOWFS), are proposed by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish (AJS) algorithm. The MOWFS-AJS is a model-specific approach whereas the FOWFS-AJS is a feature-specific approach for optimizing the weights chosen for obtaining optimal feature sets. The performances of those three proposed feature selection strategies are evaluated using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers and the performance are measured through accuracy, precision, sensitivity, and F1-score. Additionally, the area under the receiver operating characteristics curves (AUC-ROC) is plotted and it is observed that FOWFS-AJS shows the best accuracy performance based on the SVM with 94.05% and 94.90%, respectively, for HAM 10000 and BCN 20000 datasets. Finally, the experimental results are also analyzed using a non-parametric Friedman statistical test and the computational times are recorded; the results show that, out of those three proposed feature selection strategies, the FOWFS-AJS performs very well because its quick converging nature is inculcated with the help of AJS.

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A novel approach for skin lesion symmetry classification with a deep learning model

Cited by 20 publications

References 22 publications

Class-Specific Distribution Alignment for semi-supervised medical image classification

Class-Specific Distribution Alignment for semi-supervised medical image classification

Quantum computing meets skin cancer diagnosis

Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

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