Unsupervised Feature Elimination via Generative Adversarial Networks: Application to Hair Removal in Melanoma Classification

Skin cancer is one of the most threatening cancers, which spreads to the other parts of the body if not caught and treated early. During the last few years, the integration of deep learning into skin cancer has been a milestone in health care, and dermoscopic images are right at the center of this revolution. This review study focuses on the state-of-the-art automatic diagnosis of skin cancer from dermoscopic images based on deep learning. This work thoroughly explores the existing deep learning and its application in diagnosing dermoscopic images. This study aims to present and summarize the latest methodology in melanoma classification and the techniques to improve this. We discuss advancements in deep learningbased solutions to diagnose skin cancer, along with some challenges and future opportunities to strengthen these automatic systems to support dermatologists and enhance their ability to diagnose skin cancer. INDEX TERMSSkin cancer, Dermoscopy images, Deep learning, Classification, Literature review.FIGURE 1: Global heat map showing estimated agestandardized incidence rates, in 2020, of melanoma of the skin in all sexes, all ages. The map shows melanoma incidence in all parts of the world, except Greenland in the Arctic Circle. The regions most affected by skin melanoma globally are Europe, the United States, Canada, and Australia [2].microscopy-based tool to improve non-invasive diagnostic discrimination of skin lesions based on color and structure analysis [5]. This paper focuses on dermoscopy images. Because dermoscopic structures have direct histopathologic correlates, dermoscopic images help the dermatologist select management and treatment options for particular types of skin cancers [6]. In addition, dermoscopy can be useful for

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“…This is also equivalent to the PPV. [27], [34], [47], [56], [58], [63]- [65], [122], [125], [154] Sensitivity (true positive rate(TPR)) Recall…”

Section: Dermoscopic Application Of Deep Learningmentioning

confidence: 99%

“…The ability of the test to correctly identify the diseased state. [27], [33]- [35], [47], [49], [56]- [59], [63], [65], [66], [122], [125], [151]- [156] Specificity (true negative rate(TNR))…”

Section: Se(t P R) = T P T P +F Nmentioning

confidence: 99%

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

et al. 2022

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“…A novel technique named meta-learner utilizes every submodel forecast and creates the last forecast outcomes. Kim et al [ 16 ] presented a novel unsupervised technique for hair extraction and estimated it on a real-world melanoma data set. During the generative adversarial learning infrastructure, hair feature is considered with coarse-grained label easily utilizing a binary classification.…”

Section: Related Workmentioning

confidence: 99%

Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images

Vaiyapuri

Balaji

Shridevi

et al. 2022

Computational Intelligence and Neuroscience

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Melanoma is a kind of skin cancer caused by the irregular development of pigment-producing cells. Since melanoma detection efficiency is limited to different factors such as poor contrast among lesions and nearby skin regions, and visual resemblance among melanoma and non-melanoma lesions, intelligent computer-aided diagnosis (CAD) models are essential. Recently, computational intelligence (CI) and deep learning (DL) techniques are utilized for effective decision-making in the biomedical field. In addition, the fast-growing advancements in computer-aided surgeries and recent progress in molecular, cellular, and tissue engineering research have made CI an inevitable part of biomedical applications. In this view, the research work here develops a novel computational intelligence-based melanoma detection and classification technique using dermoscopic images (CIMDC-DIs). The proposed CIMDC-DI model encompasses different subprocesses. Primarily, bilateral filtering with fuzzy k-means (FKM) clustering-based image segmentation is applied as a preprocessing step. Besides, NasNet-based feature extractor with stochastic gradient descent is applied for feature extraction. Finally, the manta ray foraging optimization (MRFO) algorithm with a cascaded neural network (CNN) is exploited for the classification process. To ensure the potential efficiency of the CIMDC-DI technique, we conducted a wide-ranging simulation analysis, and the results reported its effectiveness over the existing recent algorithms with the maximum accuracy of 97.50%.

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“…Then data augmentation during the training phase was applied for the model to acquire a large number of images [23]. Kim et al (2021) proposed a hair removal method from the lesion area on the image itself. Thus, coarse hair is removed by the algorithm while preserving the features of the lesion [24].…”

Section: Related Workmentioning

confidence: 99%

“…Kim et al (2021) proposed a hair removal method from the lesion area on the image itself. Thus, coarse hair is removed by the algorithm while preserving the features of the lesion [24]. Tyagi et al (2020) proposed an intelligent prognostic model for disease prediction and classification using a combination of CNN with particle swarm optimization (PSO) [25].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

Abunadi

Senan

2021

Electronics

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With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most skin diseases have similar features, which make it challenging for dermatologists to diagnose patients accurately. Therefore, machine and deep learning techniques can have a critical role in diagnosing dermatoscopy images and in the accurate early detection of skin diseases. In this study, systems for the early detection of skin lesions were developed. The performance of the machine learning and deep learning was evaluated on two datasets (e.g., the International Skin Imaging Collaboration (ISIC 2018) and Pedro Hispano (PH2)). First, the proposed system was based on hybrid features that were extracted by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and wavelet transform (DWT). Such features were then integrated into a feature vector and classified using artificial neural network (ANN) and feedforward neural network (FFNN) classifiers. The FFNN and ANN classifiers achieved superior results compared to the other methods. Accuracy rates of 95.24% for diagnosing the ISIC 2018 dataset and 97.91% for diagnosing the PH2 dataset were achieved using the FFNN algorithm. Second, convolutional neural networks (CNNs) (e.g., ResNet-50 and AlexNet models) were applied to diagnose skin diseases using the transfer learning method. It was found that the ResNet-50 model fared better than AlexNet. Accuracy rates of 90% for diagnosing the ISIC 2018 dataset and 95.8% for the PH2 dataset were reached using the ResNet-50 model.

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Unsupervised Feature Elimination via Generative Adversarial Networks: Application to Hair Removal in Melanoma Classification

Cited by 20 publications

References 69 publications

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

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