Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Li, Yuexiang; Shen, Linlin

doi:10.3390/s18020556

Cited by 465 publications

(247 citation statements)

References 47 publications

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“…It can be observed that the closer best-segmented result was that of the Guo et al [22] in terms of SEN, ACC, DIC, and JAC. For SPE, the results of Li & Shen [38] were the closest. Table 7 and Figure 9 show a comparison between the proposed method and other representative methods that used the ISBI 2017 dataset in terms of the ACC, SEN, SPE, DIC, and JAC.…”

Section: Resultsmentioning

confidence: 93%

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

et al. 2018

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Abstract:The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95. 24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.

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Section: Resultsmentioning

confidence: 93%

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

et al. 2018

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“…A deep-learning (DL) model is the package that includes the CNN topology c nn , the CNN learning algorithm t(c nn ), and the set of labeled and conditioning examples T . DL models have recently surpassed human performance in image classification from basic to complex tasks [17,25]. A variation of DL models is using a CNN method only for feature extraction, as in the proposal of Nejati et al [24].…”

Section: Preliminariesmentioning

confidence: 99%

“…The features are labeled by the RandomForest classifier, which segments dermatological wounds with 89.87% accuracy [12]. Those classifier-driven segmentation approaches also provide the basis for measurements of the size and stage of the wound [4,8,9,10,11,14,15].Recently, deep-learning (DL) models have been successfully applied to specific tissue segmentation problems, such as skin cancer and melanoma characterization [16,17,18]. They usually rely on convolutional neural networks (CNNs) for combining both feature engineering and data classification into a single package, which eliminates the need for data extraction.…”

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A superpixel-driven deep learning approach for the analysis of dermatological wounds

Blanco¹,

Traina²,

Traina³

et al. 2020

Computer Methods and Programs in Biomedicine

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Background. The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU , that combines deep learning models with superpixel-driven segmentation methods for assessing the quality of tissues from dermatological ulcers. Method. QTDU consists of a three-stage pipeline for the obtaining of ulcer segmentation, tissues' labeling, and wounded area quantification. We set up our approach by using a real and annotated set of dermatological ulcers for training several deep learning models to the identification of ulcered superpixels. Results. Empirical evaluations on 179,572 superpixels divided into four classes showed QTDU accurately spot wounded tissues (AUC = 0.986, sensitivity = 0.97, and specificity = 0.974) and outperformed machinelearning approaches in up to 8.2% regarding F1-Score through fine-tuning of a ResNet-based model. Last, but not least, experimental evaluations also showed QTDU correctly quantified wounded tissue areas within a 0.089 Mean Absolute Error ratio. Conclusions. Results indicate QTDU effectiveness for both tissue segmentation and wounded area quantification tasks. When compared to existing machine-learning approaches, the combination of superpixels and deep learning models outperformed the competitors within strong significant levels. can be automatically evaluated by Computer-Aided Diagnosis (CAD) tools, or even used for the searching of massive databases through content-only queries, as in Content-Based Image Retrieval (CBIR) applications. In both CAD and CBIR cases, the detection of abnormalities requires the extraction of patterns from images, while a decision-making strategy is necessary for juxtaposing new images to those in the database [4,5].Since dermatological lesions are routinely diagnosed by biopsies and surrounding skin aspects, ulcers can be computationally characterized by particular types of tissues (and their areas) within the wounded region [6,7]. For instance, Mukherjee et al.[8] proposed a five-color classification model and applied a color-based low-level extractor further labeled by a Support-Vector Machine (SVM) strategy at an 87.61% hit ratio. Such idea of concatenating feature extraction and classification is found at the core of most wound segmentation strategies, as in the study of Kavitha et al.[9] that evaluated leg ulcerations by extracting patterns based on local spectral histograms to be labeled by a Multi-Layer Perceptron (MLP) classifier with 87.05% accuracy. Analogously, Pereyra et al.[10] discussed the use of color descriptors and an Instancebased Learning (IbL) classifier with a 61.7% hit ratio, whereas Veredas et al. [11] suggested the use of texture descriptors and an MLP classifier with 84.84% accuracy.Blanco et al.[4] and Chino et al.[12] followed a slightly different premise for finding proper similarity measures and comparison criteria for dermatological wounds. Their approaches are based on a divide-andconquer stra...

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“…Some investigations attempted to apply low-level hand-crafted features to distinguish melanomas from non-melanoma skin lesions [4]. Some researchers employed CNNs for melanoma classification, aiming at taking advantage of their discrimination capability to achieve performance gains [5,6]. There is still much room to improve the accuracy of melanoma recognition by combining CNNs and clinical criteria.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations

Wu²,

Jiang

et al. 2018

Preprint

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Abstract. Skin lesion is a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of skin lesion is extremely challenging manually visualization. Hence, reliable automatic classification of skin lesions is meaningful to improve pathologists' accuracy and efficiency. In this paper, we proposed a two-stage method to combine deep learning features and clinical criteria representations to address skin lesion automated diagnosis task. Stage 1 -feature encoding: Modified deep convolutional neural networks (CNNs, in this paper, we used Dense201 and Res50) were fine-tuned to extract rich image global features. To avoid hair noisy, we developed a lesion segmentation U-Net to mask out the decisive regions and used the masked image as CNNs inputs. In addition, color features, texture features and morphological features were exacted based on clinical criteria; Stage 2 -features fusion: LightGBM was used to select the salient features and model parameters, predicting diagnosis confidence for each category. The proposed deep learning frameworks were evaluated on the ISIC 2018 dataset. Experimental results show the promising accuracies of our frameworks were achieved.

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Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Cited by 465 publications

References 47 publications

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

A superpixel-driven deep learning approach for the analysis of dermatological wounds

Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations

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