Detection of skin cancer ”Melanoma” through computer vision

Cueva, Wilson F.; Muñoz, Francisco Álvaro; Vasquez, Gabrielle; Delgado, Gabriel

doi:10.1109/intercon.2017.8079674

Cited by 20 publications

(10 citation statements)

References 4 publications

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“…The proposed approach was compared with three state-of-art technique and was evaluated through the metrics of sensitivity, specificity, Jaccard index and balanced accuracy. [ [4] showed that for the early detection of melanoma advanced Technology have allowed use. In his work, an image processing was used to obtain Asymmetry, Border, Colour, and Diameter (ABCD of melanoma).For the classification of the different kinds of moles, neural network used.…”

Section: Methodsmentioning

confidence: 99%

Surveys on Detection of Melanoma through Image Processing Techniques

Shwetha¹

2018

IJRASET

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In order to achieve an effective way to identify malignant melanoma stage without performing more number of skin biopsies, digital image at an early analysis of the images is investigated. For the detection of malignant melanoma, appropriate analyses are done on the tumour images according to the clinical characteristics that early melanoma possesses. In the last stage, classification is done based on the results obtained from the above mentioned analyses. The tumours are classified as either "potential malignant melanoma" or "non-melanoma". This can reduce the time spent and pain received by the patients in detecting early malignant melanoma.

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Section: Methodsmentioning

confidence: 99%

Surveys on Detection of Melanoma through Image Processing Techniques

Shwetha¹

2018

IJRASET

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“…Comparison of performance showed that the LM algorithm achieved the highest specificity score (95.1%) and remained efficient at the classification of benign lesions, while the SCG learning algorithm produced better results if the number of epochs was increased, scoring a 92.6% sensitivity value. A mole classification system for the early diagnosis of melanoma skin cancer was proposed [19]. The proposed system extracted features according to the ABCD rule of lesions.…”

Section: Artificial Neural Network (Ann)-based Skin Cancer Detection Techniquesmentioning

confidence: 99%

Skin Cancer Detection: A Review Using Deep Learning Techniques

Dildar

Akram

Irfan

et al. 2021

IJERPH

278

109

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Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

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“…Cueva et al [34] proposed a method for skin cancer detection using computer systems. In their method, figure handling was created to produce the asymmetry, border, color, and diameter of melanoma by utilising neural systems to group various types of moles.…”

Section: Related Workmentioning

confidence: 99%

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

Abdulhamid

Şahiner

Rahebi

2020

BioMed Research International

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In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed by utilising Bezier curves. This function achieves all filled function properties. The proposed optimization method is applied to find the threshold values in melanoma skin cancer images. The proposed algorithm is implemented on PH2, ISBI2016 challenge, and ISBI 2017 challenge datasets for melanoma segmentation. The results show that the proposed algorithm exhibits high accuracy, sensitivity, and specificity compared with other methods.

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Detection of skin cancer ”Melanoma” through computer vision

Cited by 20 publications

References 4 publications

Surveys on Detection of Melanoma through Image Processing Techniques

Surveys on Detection of Melanoma through Image Processing Techniques

Skin Cancer Detection: A Review Using Deep Learning Techniques

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

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