Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network

Ali, Ibrahim; Mohamed, Mamdouh; Mahdy, Yousef B.

doi:10.48550/arxiv.1910.11960

Cited by 8 publications

(5 citation statements)

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“…In classification problems, the model has to draw boundaries between classes. If the model does not have enough data to differentiate between classes, it starts confusing class boundaries, decreasing its performance [ 16 , 21 , 45 ]. A comparison of previous studies on the HAM10000 dataset is presented in Table 5 .…”

Section: Discussionmentioning

confidence: 99%

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

et al. 2022

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Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

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Section: Discussionmentioning

confidence: 99%

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

et al. 2022

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“…This method was tested on the ISIC 2018 dataset and achieved an accuracy of 95.2%. Using the same dataset, Ali et al [21] proposed progressive generative adversarial networks (PGANs) and achieved an accuracy of 70.1%.…”

Section: Related Workmentioning

confidence: 99%

Two-Stage Input-Space Image Augmentation and Interpretable Technique for Accurate and Explainable Skin Cancer Diagnosis

Supriyanto,

Salam,

Zeniarja

et al. 2023

Computation

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This research paper presents a deep-learning approach to early detection of skin cancer using image augmentation techniques. We introduce a two-stage image augmentation process utilizing geometric augmentation and a generative adversarial network (GAN) to differentiate skin cancer categories. The public HAM10000 dataset was used to test how well the proposed model worked. Various pre-trained convolutional neural network (CNN) models, including Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19, were employed. Our approach demonstrates an accuracy of 96.90%, precision of 97.07%, recall of 96.87%, and F1-score of 96.97%, surpassing the performance of other state-of-the-art methods. The paper also discusses the use of Shapley Additive Explanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.

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“…Cıcero et al [49] leveraged images downloaded from DermWeb [50], a digital atlas containing a list of dermatology related links. Ali et al [51] created their own dataset through the use of Generative Adversarial Networks (GANs) with self-attention mechanisms, in order to generate realistic skin lesion samples to combat the frequent problem of unbalanced skin cancer datasets. As well, Hagerly et al [41] used the HAM10000 [46] dataset in their study, a database containing over 9000 images labelled with five classes of skin cancer.…”

Section: Related Workmentioning

confidence: 99%

Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images

Lee

Pavlova

Famouri³

et al. 2022

BMC Med Imaging

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Background Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective early detection with key screening approaches such as dermoscopy examinations, leading to stronger recovery prognoses. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce Cancer-Net SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public. To the best of the authors’ knowledge, Cancer-Net SCa comprises the first machine-driven design of deep neural network architectures tailored specifically for skin cancer detection, one of which leverages attention condensers for an efficient self-attention design. Results We investigate and audit the behaviour of Cancer-Net SCa in a responsible and transparent manner through explainability-driven performance validation. All the proposed designs achieved improved accuracy when compared to the ResNet-50 architecture while also achieving significantly reduced architectural and computational complexity. In addition, when evaluating the decision making process of the networks, it can be seen that diagnostically relevant critical factors are leveraged rather than irrelevant visual indicators and imaging artifacts. Conclusion The proposed Cancer-Net SCa designs achieve strong skin cancer detection performance on the International Skin Imaging Collaboration (ISIC) dataset, while providing a strong balance between computation and architectural efficiency and accuracy. While Cancer-Net SCa is not a production-ready screening solution, the hope is that the release of Cancer-Net SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

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Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network

Cited by 8 publications

References 0 publications

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

Two-Stage Input-Space Image Augmentation and Interpretable Technique for Accurate and Explainable Skin Cancer Diagnosis

Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images

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