Dermoscopic Image Segmentation using Machine Learning Algorithm

Suresh, L. Padma; Shunmuganathan, K. L.; Veni, S.

doi:10.3844/ajassp.2011.1159.1168

Cited by 5 publications

(4 citation statements)

References 11 publications

(11 reference statements)

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“…Different parameters were used to analyze the performance of various methods. They are False Positive Error (FPE), False Negative Error (FNE) and True Detection Rate (TDR) Coefficient of similarity and spatial overlap [12]. To define the first two types of quality metrics let SR denote the result of an automatic segmentation method and GT denote the ground truth segmentation obtained by the medical expert.…”

Section: Simulation Resultsmentioning

confidence: 99%

Iterative Threshoding and Morphology Operation based Melanoma Image Segmentation

Miry¹

2015

IJCA

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Dermoscopy is a suitable diagnostic technique for biology observation of pigmented skin lesions used in dermatology. Nowdays there is great interest in the prospects for methods of automatic image analysis for dermoscopy image, it Provide quantitative information about the lesion, which can be of link the doctor, and as a standalone early warning tool. This paper presents a good method of melanoma images segmentation. It based on threshoding as segmentation and mathematical morphology used to remove unwanted part in order to obtain a better segmentation. The proposed method is compared with the famous method of segmentation of skin lesions in images dermoscopic such adaptive thresholding and fuzzy K-means clustering for the segmentation and evaluated with two metrics, False Positive Rate (FPR) and the False Negtive Rate (FNR) , using the segmentation results obtained by a dermatologist experienced and ground truth.

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Section: Simulation Resultsmentioning

confidence: 99%

Iterative Threshoding and Morphology Operation based Melanoma Image Segmentation

Miry¹

2015

IJCA

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“…The ML process initially requires segmenting the lesion from the surrounding skin to isolate it; this might be critical, because in some cases, there is not a clear boundary limiting the lesion. Some of the segmentation algorithms used are built from Otsu’s thresholding, k-means, fuzzy c-means, and neural networks [ 108 , 109 ]. Most of ML approaches try to reproduce physicians’ assessments, the well-known ABCDE rule, and because of that, the next step consists in feature extraction of morphological (e.g.…”

Section: Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

“…There is not an ideal classification algorithm that outperforms the others as it depends on the dataset, image segmentation, and feature extraction (see Figure 9 ); however, in the literature, some of them have been found to provide better discrimination among skin lesions. Support Vector Machine (SVM) is the most applied classifier, as it shows the best performance [ 107 , 109 , 110 , 111 , 112 , 114 , 115 , 116 , 117 , 118 ] followed by k-Nearest Neighbors (KNN) [ 65 , 106 , 107 , 108 , 109 , 110 , 111 , 116 , 117 , 118 ], Neural Networks (NN) [ 110 , 114 , 115 , 117 , 119 ], Random Forest (RF) [ 107 , 109 , 113 , 114 ] and Decision Trees (DT) [ 65 , 109 , 110 , 111 , 117 , 118 ]. SVMs are based on statistics to build hyperplanes that maximize the distance between sets of data points [ 120 , 121 ].…”

Section: Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Rey-Barroso

Peña-Gutiérrez

Yáñez

et al. 2021

Sensors

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The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.

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“…Recently, in [24] an effective pre-processing stage is proposed, where some undesirable parts of the image such as specular reflection, dermoscopic gel, and intrinsic cutaneous features (hair, blood vessels, skin lines, and ruler markings) were removed using different filters. In [25] a diagnostic of melanoma using machine learning algorithms is reported. This study explains the task of segmenting skin lesions in dermoscopic images based on intelligent systems such as Fuzzy and Neural Networks clustering techniques for the early diagnosis of Malignant Melanoma.…”

Section: Introductionmentioning

confidence: 99%