Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images

Bechelli, Solene; Delhommelle, Jérôme

doi:10.3390/bioengineering9030097

Cited by 78 publications

(37 citation statements)

References 56 publications

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“…Bechelli et al. ( 25 ) performed a binary classification to identify the benign and malignant classes of skin cancer from dermoscopic images using machine learning and deep learning images. For the study, two datasets were used, i.e., ISIC archive and HAM10000.…”

Section: Literature Reviewmentioning

confidence: 99%

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

et al. 2022

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Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method.Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the region of interest (ROI), region-based segmentation, morphological gradient, and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A principle component analysis (PCA)-based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network-based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.

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Section: Literature Reviewmentioning

confidence: 99%

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

et al. 2022

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“…Regardless of current research to design decision support systems, there is much room to facilitate the scientists to explore and suggest efficient and effective decision support systems for acknowledgment in the field of medicine [12][13][14]. e method for the detection in the early stages is mainly concerned with the clinical characteristics utilized for image processing.…”

Section: Related Workmentioning

confidence: 99%

Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions

Mustafa

Iqbal

Rana

et al. 2022

Computational Intelligence and Neuroscience

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Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method’s reliability which shows encouraging results.

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“…In the human body, skin is the important part and is responsible for blocking heat, infections, sunlight, and wounds. There are three layers of skin such as hypodermis, dermis and epidermis [3]. Epidermis is the outmost layer of the skin that generates the skin tone.…”

Section: Introductionmentioning

confidence: 99%

Skin Disease Prediction Machine Learning Model Using Ensemble Classifier with PCA

2023

Data Analytics and Artificial Intelligence

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In the medical era, skin disease is considered one of the most common diseases among humans. Skin cancer is the most dangerous type, which can be curable if identified at the initial stage. The severity of skin cancer and the rapid count of affected people make it necessary to introduce an automatic detection scheme. Generally, analyzing and identifying skin disease in a short time is the most complex and challenging task. Several deep learning (DL) and machine learning (ML) are introduced to achieve this. However, the still fulfilling the skin cancer diagnosis is not accomplished completely. To achieve this, we proposed a machine learning model using an ensemble classifier with PCA to predict skin disease with maximum accuracy. The proposed Ensemble classifier is based on similar features and classifies several stages. It is executed by labeling vertebral disorder images according to these statistical features. The performance obtained by the ensemble classifier is compared with Support Vector Machine (SVM) and Resent with several evaluation metrics. The analysis shows that the accuracy attained by the proposed ensemble classifier is 97 % which is far better than the others in terms of classification and accuracy.

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Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images

Cited by 78 publications

References 56 publications

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions

Skin Disease Prediction Machine Learning Model Using Ensemble Classifier with PCA

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