Multi-features extraction based on deep learning for skin lesion classification

Benyahia, Samia; Meftah, Boudjelal; Lézoray, Olivier

doi:10.1016/j.tice.2021.101701

Cited by 66 publications

(30 citation statements)

References 78 publications

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“…In addition, a number of scholars have also used deep learning with machine learning to achieve good skin classification results. In Benyahia et al's study, 29 they used 17 CNN architectures as feature extractors and 24 machine learning classifiers to evaluate the efficiency of skin lesion classification from two different datasets, ISIC 2019 and

{PH}^{2}

. Through the experiments, for the ISIC 2019 dataset, DenseNet 201 combined with Fine KNN or Cubic SVM has the highest accuracy (92.34% and 91.71%).…”

Section: Related Workmentioning

confidence: 99%

Classification of skin lesions with generative adversarial networks and improved MobileNetV2

Wang

Sun

et al. 2023

Int J Imaging Syst Tech

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Malignant skin lesions pose a great threat to patients' health, and the use of computer algorithms for automatic skin medical image classification can effectively improve the efficiency of clinical diagnosis. However, the existing methods for skin classification have complex models and are greatly affected by the imbalance of the dataset. In this work, we propose a two‐stage framework called G‐DMN, it uses CycleGAN to expand the dataset and Dense‐MobileNetV2 (DMN) to achieve the automatic classification of skin lesion images. In the first stage, we use CycleGAN for data augmentation and propose a new image pairing strategy for training. Image pairs are formed from majority class images and minority class images, generators are trained for majority to minority class image conversion, and then minority class images are generated to balance the dataset. In the second stage, we propose a lightweight model called DMN by improving MobileNetV2, it enhances feature reuse by increasing the width of the network and allows the network to focus on focal areas from different scales. The original training set combined with the generated images is used to train DMN for skin lesion classification. We tested the proposed model on the HAM10000 dataset, and the G‐DMN achieved 87.07% classification accuracy, 80.13% precision, 75.28% sensitivity, 96.19% specificity, 77.26% F1‐Score and 0.952 AUC, which has a good classification effect, while the number of parameters of the model is only 5.33 M, which is much lower than other classical classification models. We demonstrate that the proposed method is lighter and more effective than classical classification methods, achieving significant performance improvements.

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{PH}^{2}

. Through the experiments, for the ISIC 2019 dataset, DenseNet 201 combined with Fine KNN or Cubic SVM has the highest accuracy (92.34% and 91.71%).…”

Section: Related Workmentioning

confidence: 99%

Classification of skin lesions with generative adversarial networks and improved MobileNetV2

Wang

Sun

et al. 2023

Int J Imaging Syst Tech

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“…Then, the output results are concatenated and forwarded to the next convolutional layer. Inception units produce multilevel feature extraction with optimized variants and avoid patch alignment problems 60 , 61 . They also reduce the number of network parameters.…”

Section: Using Cgan In Fine-tuning Transfer Learning Models For Few-s...mentioning

confidence: 99%

A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasks

Elaraby

Barakat

Rezk

2022

Sci Rep

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Supervised learning with the restriction of a few existing training samples is called Few-Shot Learning. FSL is a subarea that puts deep learning performance in a gap, as building robust deep networks requires big training data. Using transfer learning in FSL tasks is an acceptable way to avoid the challenge of building new deep models from scratch. Transfer learning methodology considers borrowing the architecture and parameters of a previously trained model on a large-scale dataset and fine-tuning it for low-data target tasks. But practically, fine-tuning pretrained models in target FSL tasks suffers from overfitting. The few existing samples are not enough to correctly adjust the pretrained model’s parameters to provide the best fit for the target task. In this study, we consider mitigating the overfitting problem when applying transfer learning in few-shot Handwritten Character Recognition (HCR) tasks. A data augmentation approach based on Conditional Generative Adversarial Networks is introduced. CGAN is a generative model that can create artificial instances that appear more real and indistinguishable from the original samples. CGAN helps generate extra samples that hold the possible variations of human handwriting instead of applying traditional image transformations. These transformations are low-level, data-independent operations, and only produce augmented samples with limited diversity. The introduced approach was evaluated in fine-tuning the three pretrained models: AlexNet, VGG-16, and GoogleNet. The results show that the samples generated by CGAN can enhance transfer learning performance in few-shot HCR tasks. This is by achieving model fine-tuning with fewer epochs and by increasing the model’s $$F1-score$$ F 1 - s c o r e and decreasing the Generalization Error $$(E_{test})$$ ( E test ) .

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“…The authors tried to obtain the mid-level features by studying the relationships among different images based on distance metric learning and gave as an input to the classifiers instead of using the extracted features directly. A study on understanding the efficiency of 17 commonly pre-trained CNN models used for feature extraction was carried out by Samia Benyahia et al [ 38 ]. It has been observed that DenseNet201 along with k -nearest neighbor and support sector machine (SVM) outperformed with respect to classification accuracy for the ISIC 2019 dataset.…”

Section: Literature Surveymentioning

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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Multi-features extraction based on deep learning for skin lesion classification

Cited by 66 publications

References 78 publications

Classification of skin lesions with generative adversarial networks and improved MobileNetV2

Classification of skin lesions with generative adversarial networks and improved MobileNetV2

A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasks

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

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