Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features

Yang, Shuang; Shu, Chen; Hu, Haiyou; Ma, Guanghui; Yang, Min

doi:10.1155/2022/9726181

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Researchers are improving algorithms to automatically standardize images of skin lesions to avoid errors in diagnosis due to perturbations and imperfect images [39,40].…”

Section: Discussionmentioning

confidence: 99%

AI in Dermatology: Bridging the Gap Between Potential and Practice

Klarycki,

Tomczyk,

Podgórska

et al. 2024

J Educ Health Sport

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Artificial intelligence has been rapidly penetrating every element of our lives for quite some time. However, its presence in health care has remained elusive. This is particularly apparent in the field of dermatology, where, given the characteristics of this discipline of medicine, it would seems that its presence should be abundant. Malignant skin lesions are still high in the statistics in terms of cancer mortality while being one of the easiest to treat when diagnosed early. There are many reasons why artificial intelligence is not used in daily practice as an aid for cancers detection. However the most important one is the ongoing insufficient quality of the algorithms, which, despite great results in laboratory settings, do not produce good enough outcomes in clinical settings. Other important reasons are that people still distrust and fear artificial intelligence and simply the legal lack of adaptation of countries to its lawful and safe use. Despite the work of scientists and legislators the road to seeing artificial intelligence as a helping tool for dermatologists on a daily basis is still very long and requires the attention of scientists and the whole medical community.

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“…Researchers are improving algorithms to automatically standardize images of skin lesions to avoid errors in diagnosis due to perturbations and imperfect images [39,40].…”

Section: Discussionmentioning

confidence: 99%

AI in Dermatology: Bridging the Gap Between Potential and Practice

Klarycki,

Tomczyk,

Podgórska

et al. 2024

J Educ Health Sport

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show abstract

“…In order to diagnose skin cancer, this project attempts to develop an efficient system for classifying dermoscopic images [52][53][54][55][56][57][58]. A modified MLP is combined with three multi-directional representation systems to create a Hybrid Artificial Intelligence Model (HAIM) for dermoscopic image categorization that successfully accomplishes this goal [59][60][61].…”

Section: Fig 3 Multidirectional Representation Systems Using Curvelet...mentioning

confidence: 99%

Multidirectional Analysis of Curvelet Against Skin Cancer

Kolekar,

Razak

et al. 2024

Preprint

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An elevated chance of getting another melanoma is associated with a personal history of the disease. Individuals who have already had a melanoma have a 2–5% probability of getting another one later. Compared to individuals whose initial melanoma was superficial spreading melanoma, those whose first melanoma was lentigo maligns melanoma or nodular melanoma are at a greater peril of emerging a secondary dominant cancer. Melanoma risk is double in those with a special antiquity of squamous cell carcinoma. The likelihood of getting melanoma is doubled if you have a particular times past of basal cell carcinoma. In addition, melanoma risk is higher in persons with actinic keratosis than in those without the condition. An automated technique for classifying melanoma, or skin cancer, is proposed in this work. An image of gathered data is used as the input for the proposed system, and various image handling methods remain smeared to improve the picture's characteristics. The curvelet technique is used to separate benign from malignant skin cancer and to collect relevant data from these pictures so that the classifier may be trained and tested. The basic wrapper curvelet's coefficients are the characteristics that are utilized for classification. Curvelet works well with images that have cartoon edges and aligned textures. In a database of digital photos, the three-layer back-propagation neural network classifier with curvelet has 75.6% recognition accuracy.

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