Detection skin cancer using SVM and snake model

Bumrungkun, Prachya; Chamnongthai, Kosin; Patchoo, Wisarn

doi:10.1109/iwait.2018.8369708

Cited by 24 publications

(7 citation statements)

References 1 publication

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“…REPEAT (14) COMPUTE mapping of template with a skin cancer image (15) COMPUTE difference between template and skin cancer image ( 16)…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, the complexity of automatic initialization remained as a problem in these mentioned methods for system implementation and to set parameters (weighting factors, iteration steps, etc.) for active contours depending on human skill [15] and it was not clearly reproducible. e authors of this paper hence have focused on the research problems of automatic initialization and parameter setting for active contours.…”

Section: Introductionmentioning

confidence: 91%

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Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation

Bumrungkun

Chamnongthai

Patchoo

2021

Journal of Healthcare Engineering

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For active-contour-based surgery systems, the success of skin cancer boundary segmentation depends on the initialization point of the snake model, which is a task originally performed by skillful experts, and on the parameters set for the algorithms of active contour. This paper proposes initial geometrical templates and parameter sets for the active contour on skin cancer boundary segmentation. To establish initial geometrical templates and parameter sets for the active contour, first, template candidates, which are geometrically designed by users in advance, are simply calculated based on similarity with a skin cancer boundary, and the candidate with the least difference is selected as an initial template. Initially, all candidate templates are performed before the test with some selected skin cancer samples by randomly varying needed parameters to determine parameter sets for each template. The parameter set is therefore implicitly selected as the suitable set with the selected initial template. Experiments with 227 skin cancer samples were performed based on our proposed initial templates and parameter sets, and the results show 99.46% accuracy, 97.43% sensitivity, and 99.87% specificity approximately in which accuracy, sensitivity, and specificity were improved by 0.26%, 0.36%, and 0.26%, respectively, compared with the conventional method.

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“…REPEAT (14) COMPUTE mapping of template with a skin cancer image (15) COMPUTE difference between template and skin cancer image ( 16)…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation

Bumrungkun

Chamnongthai

Patchoo

2021

Journal of Healthcare Engineering

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“…Bumrungkun et al [2] proposed a method to detect the skin cancer using SVM and snake model. Since the skin cancer is one of the causes for increase in deaths among all cancer, this paper tries to detect cancer by applying all the techniques in the cancer image which has asymmetry, colour variation, etc.…”

Section: Literature Surveymentioning

confidence: 99%

Skin Cancer Prediction Using Machine Learning Algorithms

Raj

Bhuvaneshwari

et al. 2021

Artificial Intelligence and Technologies

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“…To cure skin cancer, photodynamic therapy is an alternative way to cure skin cancer and to be able to reduce the pain. But surgery medicine, basically depends on a skilled medical doctor that is quite limited and usually expensive, so automatic cancer surgery system is necessary to be able to cure the patient as a reliable medical assistant [7].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Image Classification using Convolutional Neural Network

Syafaah

Hanami

Sofiani

et al. 2021

KINETIK

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Classification of skin cancer is an important task to detect skin cancer and help with the treatment of skin cancer according to its type. There are many techniques in imaging used to classify skin cancer, one of the superior deep learning (DL) algorithms for classification is the Convolutional Neural Network (CNN). One type of skin cancer is dangerous is melanoma. In this study, CNN is proposed to help classify this type of skin cancer. The dataset consists of 15103 images of skin cancer pigments with 7 different types of skin cancer. These three tests proved malignant skin lesions can be classified with higher accuracy than non-melanocytic skin lesions which is 90% and performance evaluation shows melanocytic and non-melanocytic skin lesions detected with the highest accuracy. The tests conducted in this study grouped several types of skin diseases namely the first tests conducted using a group of melanocytic and non-melanocytic skin disease, second testing using groups of melanoma and melanocytic nevus diseases, and the final testing using malignant and benign. The proposed CNN model achieved significant performance with a best accuracy of 94% on the classification of melanoma and melanocytic nevus.

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Detection skin cancer using SVM and snake model

Cited by 24 publications

References 1 publication

Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation

Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation

Skin Cancer Prediction Using Machine Learning Algorithms

Skin Lesion Image Classification using Convolutional Neural Network

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