Color channel based segmentation of skin lesion from clinical images for the detection of melanoma

Sagar, Chiranjeev; Saini, Lalit Mohan

doi:10.1109/icpeices.2016.7853624

Cited by 14 publications

(10 citation statements)

References 14 publications

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“…Utilizing automated methods to find cancer advances medical knowledge, helps to diagnose the disease early, and produces the best results quickly. The instruments used to detect skin cancer are briefly covered in the next section [19], along with our recommended methodology (Table 1).…”

Section: Related Workmentioning

confidence: 99%

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Review on Automated Skin Cancer Detection Using Image Processing Methods

Maher¹,

Bhawiskar²

2023

Advances in Computer Science Research

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The skin is the most crucial component of the human body because it protects the muscles, bones, and entire body. One of the most common illnesses affecting people nowadays is skin cancer. These days, a great number of people are affected by skin cancer. Skin cancer develops as a result of genetic flaws or mutations brought on by unrepaired deoxyribonucleic acid in skin cells. A novel spectral approach is devised to acquire a number of measurements of those discovered in malignant skin areas using the sample photos that were taken by medical researchers. The two stages of the automated diagnosis system's operation are the detection of skin abnormalities and the assessment of melanoma's malignancy. This paper outlines the procedures and approaches for automated skin cancer diagnosis. This article offers early-stage researchers helpful details on methods, databases, and the essential procedures for a skin cancer diagnosis.

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Section: Related Workmentioning

confidence: 99%

“…The dermis, which is the term for the top layer, is composed of the cellular layers Squamous, Basal, and Melanocytes. These cells shield the skin from harm [14]. Neurons, blood arteries, and sweat glands are all found in the epidermis, sometimes referred to as the inner layer.…”

Section: Introductionmentioning

confidence: 99%

Review on Automated Skin Cancer Detection Using Image Processing Methods

Maher¹,

Bhawiskar²

2023

Advances in Computer Science Research

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“…Dataset Source Total Images [24] Train: Asan, Nromal, Web, MED-NODE 220,680 Test: Dermofit, SNU 1300, 2201 [25] Train: Asan, Additional Asan, Atlas, MED-NODE, Hallym 179,027 Test: Dermofit, Asan 1300, 1276 [26] SD-198, SD-260 6584, 20,600 [27] DermQuest 22,080 [28] Digital clinical images collected at University of Tsukuba 4867 [29,30] PAD-UFES-20 2298 [31,32] PAD-UFES-20 2057 [33,34] Collected by authors 2000, NQ [35] PAD-UFES-20 1612 [36] Dermofit 1300 [37] DermIs, DermQuest, DanDerm, DrrmNet NZ, DermAtlas 1200 [38] Hellenic Dermatological Atlas, Dermatology Atlas, DermNet Nz, Interactive dermatology atlas 877 [39] DermAtlas, DermNet, DermIs, Skin Cancer and Benign Tumor Image Atlas, 408 YSP Dermatology Image Database, Saúde, skin cancer guide [40] DermIs, DermQuest 399 [41] DermIs 397 [42] DermQuest, DanDerm, DermAtlas, DermIs, DermNetNz 370 [43] dermAtlas, DermNent, DermNet NZ, DermQuest, dermIs, 282 Dermatology Atlas, National Cancer Institute [44] DermNet, DermQuest 220 [45] DermIs 207 [46][47][48][49] HLIF Dataset(Subset of DermIs, DermQuest) 206 [50] MED-NODE, Skin Vision 200 [51] DermIs, DermQuest 191 [52] Train: National Skin Center (NSC) of Singapore 184 Test: MED-NODE 170 [53] Derm1O1, DermNet, DermIS, DermQuest 175 [23,36,[54][55][56][57][58][59][60]…”

Section: Papermentioning

confidence: 99%

“…Illumination correction [43,44,[46][47][48][49]51,[53][54][55][56]59,63,65,66] Artifact removal [23,27,39,[41][42][43][53][54][55][56]59,63,67,71] Data augmentation [28,31,35,36,40,51,55,62,73] Image cropping [23,25,28,42,[46][47][48][49]69] 4.2.1. Illumination Correction (Shading Attenuation)…”

Section: Pre-processing Task Referencesmentioning

confidence: 99%

Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review

Nazari,

Garcia

2023

Life

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Skin cancer has become increasingly common over the past decade, with melanoma being the most aggressive type. Hence, early detection of skin cancer and melanoma is essential in dermatology. Computational methods can be a valuable tool for assisting dermatologists in identifying skin cancer. Most research in machine learning for skin cancer detection has focused on dermoscopy images due to the existence of larger image datasets. However, general practitioners typically do not have access to a dermoscope and must rely on naked-eye examinations or standard clinical images. By using standard, off-the-shelf cameras to detect high-risk moles, machine learning has also proven to be an effective tool. The objective of this paper is to provide a comprehensive review of image-processing techniques for skin cancer detection using clinical images. In this study, we evaluate 51 state-of-the-art articles that have used machine learning methods to detect skin cancer over the past decade, focusing on clinical datasets. Even though several studies have been conducted in this field, there are still few publicly available clinical datasets with sufficient data that can be used as a benchmark, especially when compared to the existing dermoscopy databases. In addition, we observed that the available artifact removal approaches are not quite adequate in some cases and may also have a negative impact on the models. Moreover, the majority of the reviewed articles are working with single-lesion images and do not consider typical mole patterns and temporal changes in the lesions of each patient.

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“…Gopinathan et al [4] first converts the image from RGB to grayscale and then applied wiener filter to remove noise from the image. Sagar et al [5] proposed an algorithm for pre-processing step. Firstly, resize the image to 512 x 512 pixels then adjust the gamma values to normalize the irregularity in illuminations and shadows.…”

Section: Pre-processingmentioning

confidence: 99%

Automated Skin Lesion Detection towards Melanoma

Bibi

Hamid

Tehseen³

2018

ICST Transactions on Scalable Information Systems

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Skin cancer melanoma is one of the most dangerous cancers in the world. It is crucial to diagnose it in initial phases before it invades other organs. However, it requires an efficient and reliable diagnostic computer aided system for early detection. In this research study we aim to detect the skin cancer from two different image datasets. We also present the solution for images that contain disk objects. In initial phase we perform pre-processing, which is followed by segmentation phase. Then candidate dataset is evaluated using different measures such as accuracy, specificity, sensitivity and similarity. Obtained results are compared with results of techniques used in academic literature. We claim that our techniques give better accuracy for cancer detection.

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Color channel based segmentation of skin lesion from clinical images for the detection of melanoma

Cited by 14 publications

References 14 publications

Review on Automated Skin Cancer Detection Using Image Processing Methods

Review on Automated Skin Cancer Detection Using Image Processing Methods

Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review

Automated Skin Lesion Detection towards Melanoma

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