The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks

Lee, Kin Wai; Chin, Renee Ka Yin

doi:10.1109/iicaiet49801.2020.9257859

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…For some time now, data augmentation is being adopted to increase the number of images in datasets. As in previous work [ 29 , 54 , 55 ], we have also experimented with data augmentation and CNN with both labelled and partially labelled datasets. From the results obtained, we have achieved an overall performance of 70.8% for our enhanced ResNet50 after performing data augmentation, and likewise, we have reached a performance of 64.9% for the VGG16.…”

Section: Resultsmentioning

confidence: 99%

Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN)

Khan

Sahib

Dayalen³

et al. 2022

Computational Intelligence and Neuroscience

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The lack of annotated datasets makes the automatic detection of skin problems very difficult, which is also the case for most other medical applications. The outstanding results achieved by deep learning techniques in developing such applications have improved the diagnostic accuracy. Nevertheless, the performance of these models is heavily dependent on the volume of labelled data used for training, which is unfortunately not available. To address this problem, traditional data augmentation is usually adopted. Recently, the emergence of a generative adversarial network (GAN) seems a more plausible solution, where synthetic images are generated. In this work, we have developed a deep generative adversarial network (DGAN) multi-class classifier, which can generate skin problem images by learning the true data distribution from the available images. Unlike the usual two-class classifier, we have developed a multi-class solution, and to address the class-imbalanced dataset, we have taken images from different datasets available online. One main challenge faced during our development is mainly to improve the stability of the DGAN model during the training phase. To analyse the performance of GAN, we have developed two CNN models in parallel based on the architecture of ResNet50 and VGG16 by augmenting the training datasets using the traditional rotation, flipping, and scaling methods. We have used both labelled and unlabelled data for testing to test the models. DGAN has outperformed the conventional data augmentation by achieving a performance of 91.1% for the unlabelled dataset and 92.3% for the labelled dataset. On the contrary, CNN models with data augmentation have achieved a performance of up to 70.8% for the unlabelled dataset. The outcome of our DGAN confirms the ability of the model to learn from unlabelled datasets and yet produce a good diagnosis result.

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Section: Resultsmentioning

confidence: 99%

Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN)

Khan

Sahib

Dayalen³

et al. 2022

Computational Intelligence and Neuroscience

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“…To further verify the effectiveness of our algorithm, we use a skin cancer dataset to evaluate the performance of AcneGrader, and we compare four state‐of‐the‐art methods, discretized interpretable multi‐layer perceptron (DIMLP)‐ensemble, 63 convolutional neural network (CNN), 64 three‐way decision‐based Bayesian deep learning (TWDBDL), 65 and binary residual feature fusion (BARF) (cross), 45 as shown in Table 4 . We can see that BARF(cross) can achieve the best performance.…”

Section: Resultsmentioning

confidence: 99%

AcneGrader: An ensemble pruning of the deep learning base models to grade acne

Liu

Fan

Duan

et al. 2022

Skin Research and Technology

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Background Acne is one of the most common skin lesions in adolescents. Some severe or inflammatory acne leads to scars, which may have major impacts on patients’ quality of life or even job prospects. Grading acne plays an important role in diagnosis, and the diagnosis is made by counting the number of acne. It is a labor‐intensive job and it is easy for dermatologists to make mistakes, so it is very important to develop automatic diagnosis methods. Ensemble learning may improve the prediction results of the base models, but its time complexity is relatively high. The ensemble pruning strategy may solve this computational challenge by removing the redundant base models. Materials and methods This study proposed a novel ensemble pruning framework of deep learning models to accurately detect and grade acne using images. First, we train multi‐base models and prune the redundancy models according to the performance and diversity of the models. Then, we construct the new features of the training data by the base models we select in the previous step. Next, we remove the redundancy models further by a feature selection algorithm. Finally, we integrate all the base models by classifiers. The ensemble pruning algorithm was proposed to prune the deep learning base models. Results The experimental data showed that the ensemble pruned framework achieved a prediction accuracy of 85.82% on the acne dataset, better than the existing studies. To verify our method's effectiveness, we test our method in a skin cancer dataset and greatly outperform the state‐of‐the‐art methods. Conclusion The method we proposed is used to grade acne. Our method's performance outperforms state‐of‐the‐art methods on two datasets, and it can also remove redundancy models to reduce computational complexity.

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“…released the HAM10000 dataset by natural data augmentation; the images of skin lesions were captured at various magnifications or angles, or with multiple cameras. To evaluate the effectiveness of data augmentation methods while determining the most effective method ( 87 ), explored four types of data augmentation methods (geometric transformation, adding noise, color transformation, and image mix) and a multiple-layer augmentation method (augmented images by more than one operation) in melanoma classification. The first step was to preprocess the images to remove artifacts such as body hair on the images.…”

Section: Methods For Typical and Frontier Problems In Skin Cancer Cla...mentioning

confidence: 99%

Skin Cancer Classification With Deep Learning: A Systematic Review

Chen

Zeng

et al. 2022

Front. Oncol.

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Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model’s cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers’ convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

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The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks

Cited by 12 publications

References 11 publications

Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN)

Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN)

AcneGrader: An ensemble pruning of the deep learning base models to grade acne

Skin Cancer Classification With Deep Learning: A Systematic Review

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