Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD

Mabrouk, Mai S.; Sayed, Ahmed Y.; Afifi, Heba M.; Sheha, Mariam A.; Sharwy, Amr

doi:10.1007/s41666-020-00067-3

Cited by 13 publications

(7 citation statements)

References 30 publications

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“…In this context, Mabrouk and co-researchers had presented a fully automated approach for the early diagnosis of lethality hidden in pigmented skin lesions. Finally, the total dermoscopy score (TDS) is assigned to the skin lesions based on the ABCDE assessment [ 24 ]. The CNN mainly utilized in deep learning has certain shortcomings that need to be considered illustrated by the author efficiently taking four main data sets and propounded that accuracy enhancements usually mask corruption robustness problems to an extent also the evaluation of classifiers affected distorted images [ 25 , 26 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques’ results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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“…A buzzard optimization function extraction algorithm and SVM classifier provides accuracy 94.3% and the buzzard optimization for feature extraction is awesome. Best results were obtained in [74] accuracy is 99.29%, in [77] combine pre-training CNN and multiclass SVM accuracy is 100% based on camera and computer, also in [83] using SVM best accuracy obtained 98.75%.…”

Section: Discussionmentioning

confidence: 94%

“…Through Table 1 there are some authors use preprocessing methods to enhance the image before classification stages [72,78,81], like removal hair and the line also standardization brightness unbalanced and in [76], using anisotropic diffusion filter and unsharp masking is intended to remove the visual noise such as lines and edges and improve the image information for feature extraction methods usually based on convolution neural network to extract the most [81] to generate high-level features based on combining stacked convolutional neural networks in order to extract important discrimination features. ABCD rule is preferred for checking skin diseases worldwide as a common reference, [83] proposed ABCD for extracting the features like Asymmetry, Border, and Color features, as well as the diameter. Another method to extract features based on deep learning [92], which use Encoder-decoder Fully Convolutional Network (FCN) method, is suitable for small volumes of input data and requires only a few parameters, making the method easy to interpret.…”

Section: Discussionmentioning

confidence: 99%

“…While other reviewed skin lesion classification methods were based on Optimized neutrosophic k-means (ONKM) in [74] Genetic algorithm for optimizing the value of α in α-mean operation in the neutrosophic set. Also, adaptive k-means and Random Fores in [76], Multiclass SVM in [77,78,91], SVM, ANN, KNN, and TDV in [83]. A buzzard optimization function extraction algorithm and SVM classifier provides accuracy 94.3% and the buzzard optimization for feature extraction is awesome.…”

Section: Discussionmentioning

confidence: 99%

“…Mabrouk et al [83] introduced a model composed of four essential features used to detect Melanoma, which is feasible for visual examination depends on the ABCD model. The PSL, preprocessing, and segmentation using appropriate algorithms, feature detecting will happen regarding the ABCD rule.…”

Section: Fig 9 Densenet Architecturementioning

confidence: 99%

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Skin Lesions Classification Using Deep Learning Techniques: Review

Kareem

Abdulazeez

Zeebaree

2021

AJRCoS

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Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

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