Automated Detection and Segmentation of Vascular Structures of Skin Lesions Seen in Dermoscopy, With an Application to Basal Cell Carcinoma Classification

Kharazmi, Pegah; AlJasser, Mohammed I.; Lui, Harvey; Wang, Z. Jane; Lee, Tim K.

doi:10.1109/jbhi.2016.2637342

Cited by 76 publications

(68 citation statements)

References 22 publications

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“…Additionally, random forests (RF) learning methods have also been applied to dermatoscopy images for both melanoma and BCC classification. Ferris et al constructed a model of 1000 decision trees and a threshold for malignant diagnosis of 0.4, with sensitivity results higher than physicians and specificity lower, while Kharazmi et al explored the use of vascular features for basal cell carcinoma automatic detection with 100 trees. No reference is made to the reasons considered for tree number selection.…”

Section: Resultsmentioning

confidence: 99%

The role of AI classifiers in skin cancer images

Magalhães

Mendes

Vardasca

2019

Skin Research and Technology

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Background The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.

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Section: Resultsmentioning

confidence: 99%

The role of AI classifiers in skin cancer images

Magalhães

Mendes

Vardasca

2019

Skin Research and Technology

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“…The hair existence is a major problem in dermoscopic images, in this, if the skin lesion is covered by hair it will be difficult for segmentation, pattern recognition, and classification task. Morphological filtering is the best technique for removing the hair particle on skin lesion [9]. In figure (a) shows the result of Gaussian filter and DullRazor filtering applied on dermoscopic images for removing the noise and hair on skin lesion respectively.…”

Section: B Image Pre-processingmentioning

confidence: 99%

Skin Lesion Detection using Texture Based Segmentation and Classification by Convolutional Neural Networks (CNN)

L*,

Sagar,

2019

IJITEE

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Skin cancer is one of the dangerous cancers like breast cancer, brain tumour, and lung cancer. The detection of a skin lesion is melanoma or nonmelanoma is a very crucial issue. The earlier detection of melanoma is one of the best solutions for this issue. There is a various technique for detecting the skin lesion. Because of the technology advancement earlier detection of the skin lesion is possible. Malignant melanoma is a very harmful melanoma it is the cancerous cell that will lead to growth and that can be a mole in different colours red, black and brown. Skin lesion segmentation from dermoscopic images is a very challenging task nowadays because of the contrast of those images. there are various techniques for detecting the skin cancer base on the characteristics of the images shape, colour, textures. We proposed a system for skin cancer detection using texture-based segmentation and classification using Convolutional Neural Network. GLCM (Gray Level Co-occurrence Matrix) matrix is exacting the features from an image. And used Neural network tool for checking the accuracy of training network. Nowadays Deep Learning technique is very popular for classification of images. CNN is one of the techniques of Deep Learning. The proposed work will help in classification of skin lesion. Model will helpful for dermatologists for classifying melanomas.

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“…The values examined by the authors are depicted in table II. [13] has been presented. From the figure 11, it is clear that the TPR of the proposed is higher about 1.71% than the existing work.…”

Section: A Comparison Of Proposed Work With Existing Workmentioning

confidence: 99%

An Enhanced and Automatic Skin Cancer Detection using K-Mean and PSO Technique

2019

IJITEE

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Scientists have been trying to implement traditional methods around the world, particularly in developing countries, to reduce the death rate of skin cancer in humans. The scientific term is named as melanoma. But this effort always working hard as the system is costly, the low availability of experts and the conventional telemedicine. There are three types of skin cancer: basal cell cancer (BCC), squamous cell cancer, and melanoma. More than 90% of human is affected by ultraviolet (UV) radiation exposed to the sun. In this research, a skin cancer detection system (BCC) is designed in MATLAB. The images going to different processes such as Pre processing, feature extraction and classification. In pre-processing K-mean clustering is applied to determine the foreground and background of an image, since some part of background appear in the image after K-mean. Therefore, to resolve this problem Particle Swarm optimization (PSO) is applied. The segmented image features are extracted using Speed Up Robust Features (SURF), this helps to enhance the quality of the image. The Artificial neural network (ANN) is trained on the basis of these extracted features. To determine the efficiency of the system, the images are tested and performance parameters are measured. The detection accuracy determined by this model is about 98.7 5 is obtained.

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Automated Detection and Segmentation of Vascular Structures of Skin Lesions Seen in Dermoscopy, With an Application to Basal Cell Carcinoma Classification

Cited by 76 publications

References 22 publications

The role of AI classifiers in skin cancer images

The role of AI classifiers in skin cancer images

Skin Lesion Detection using Texture Based Segmentation and Classification by Convolutional Neural Networks (CNN)

An Enhanced and Automatic Skin Cancer Detection using K-Mean and PSO Technique

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