State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: a comprehensive review

Bhatt, Harsh; Shah, Vrunda; Shah, Krish; Shah, Ruju; Shah, Manan

doi:10.1016/j.imed.2022.08.004

Cited by 40 publications

(14 citation statements)

References 41 publications

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“…The identification of skin cancer is a complex procedure. A skilled dermatologist makes a diagnosis through a series of steps, starting with the naked eye identifying abnormal cancerous cells, followed by the dermoscopy that employs an optical lens for analyzing abnormalities in extreme detail, the biopsy is performed [31][32][33][34][35][36][37][38]73]. Skin cancer is a significant concern that demands quick evaluation from medical professionals [74,75].…”

Section: Discussionmentioning

confidence: 99%

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

Naeem,

Anees

2024

PLoS ONE

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Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model’s accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.

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Section: Discussionmentioning

confidence: 99%

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

Naeem,

Anees

2024

PLoS ONE

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“…The user chooses a parameter called K. Hence, K encompasses all possible scenarios, finds novel scenarios in related categories, and detects every comparable extant feature example with new cases. As a result, selecting the value of K is crucial and needs careful consideration; see the finding in [20,32]. The use of KNNs to identify the abnormal formation of skin lesions has broadened their range of applications; see [33].…”

Section: K Nearest Neighbors (Knn)mentioning

confidence: 99%

“…Data exits the output node after passing through the hidden layer and enters through the input layer. Convolutional neural network (CNN) is an adaptation of the deep feedforward ANN that is widely used for image processing; here, we refer to the works [20][21][22]. CNNs are a particular category of ANNs, and this needs to be made clear.…”

Section: Introductionmentioning

confidence: 99%

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Hussain,

Toscano

2024

Symmetry

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Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin cancer has a higher fatality rate, and it expands gradually to different areas of the body. The rapid growth of automated diagnosis frameworks has led to the combination of diverse machine learning, deep learning, and computer vision algorithms for detecting clinical samples and atypical skin lesion specimens. Automated methods for recognizing skin cancer that use deep learning techniques are discussed in this article: convolutional neural networks, and, in general, artificial neural networks. The recognition of symmetries is a key point in dealing with the skin cancer image datasets; hence, in developing the appropriate architecture of neural networks, as it can improve the performance and release capacities of the network. The current study emphasizes the need for an automated method to identify skin lesions to reduce the amount of time and effort required for the diagnostic process, as well as the novel aspect of using algorithms based on deep learning for skin lesion detection. The analysis concludes with underlying research directions for the future, which will assist in better addressing the difficulties encountered in human skin cancer recognition. By highlighting the drawbacks and advantages of prior techniques, the authors hope to establish a standard for future analysis in the domain of human skin lesion diagnostics.

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“…While papers [96] and [98] have previously examined deep learning methods applied to skin cancer classification, our analysis extends to include subdivisions like supervised, semi-supervised, reinforcement learning, and ensemble methods. Unlike review [97], which focused solely on Convolutional Neural Networks (CNNs) within the deep learning spectrum, our examination encompasses a broader range of models, including transformers. Moreover, we explore the forefront of each specific machine learning and deep learning domain, such as K-Nearest Neighbors (KNN), Decision Trees, Support Vector Machines (SVM), and different learning models-supervised, semi-supervised, reinforcement learning, and ensemble methods.…”

Section: Introductionmentioning

confidence: 99%

Advancements in skin cancer classification: a review of machine learning techniques in clinical image analysis

Yang,

Luo,

Greer

2024

Multimed Tools Appl

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Early detection of skin cancer from skin lesion images using visual inspection can be challenging. In recent years, research in applying deep learning models to assist in the diagnosis of skin cancer has achieved impressive results. State-of-the-art techniques have shown high accuracy, sensitivity and specificity compared with dermatologists. However, the analysis of dermoscopy images with deep learning models still faces several challenges, including image segmentation, noise filtering and image capture environment inconsistency. After making the introduction to the topic, this paper firstly presents the components of machine learning-based skin cancer diagnosis. It then presents the literature review on the current advance in machine learning approaches for skin cancer classification, which covers both the traditional machine learning approaches and deep learning approaches. The paper also presents the current challenges and future directions for skin cancer classification using machine learning approaches.

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State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: a comprehensive review

Cited by 40 publications

References 41 publications

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Advancements in skin cancer classification: a review of machine learning techniques in clinical image analysis

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