Artificial Intelligence in Skin Cancer: Diagnosis and Therapy

Das, Trishala; Kumar, Vijay; Prakash, Amresh; Lynn, Andrew M.

doi:10.1007/978-981-16-0364-8_9

Cited by 5 publications

(3 citation statements)

References 117 publications

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“…Computational and immunogenomic approaches for cancer immunotherapies are already being discussed for different types of cancer [ 686 , 687 , 688 , 689 ]. AI tools are already used to assist in prognostication or the prediction of treatment responses of melanoma patients to immunotherapy [ 690 ] and in general diagnosis and therapeutic predictions [ 30 , 343 , 688 , 690 , 691 , 692 , 693 , 694 , 695 ]. Furthermore, recent advances in integrating spatial transcriptomics with histology promises to revolutionize pathological diagnosis of tumors [ 696 ], including melanoma (see below).…”

Section: Role Of Vitamin D In Melanomamentioning

confidence: 99%

Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling

Slominski,

Kim,

Janjetovic

et al. 2024

Cancers

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Melanoma, originating through malignant transformation of melanin-producing melanocytes, is a formidable malignancy, characterized by local invasiveness, recurrence, early metastasis, resistance to therapy, and a high mortality rate. This review discusses etiologic and risk factors for melanoma, diagnostic and prognostic tools, including recent advances in molecular biology, omics, and bioinformatics, and provides an overview of its therapy. Since the incidence of melanoma is rising and mortality remains unacceptably high, we discuss its inherent properties, including melanogenesis, that make this disease resilient to treatment and propose to use AI to solve the above complex and multidimensional problems. We provide an overview on vitamin D and its anticancerogenic properties, and report recent advances in this field that can provide solutions for the prevention and/or therapy of melanoma. Experimental papers and clinicopathological studies on the role of vitamin D status and signaling pathways initiated by its active metabolites in melanoma prognosis and therapy are reviewed. We conclude that vitamin D signaling, defined by specific nuclear receptors and selective activation by specific vitamin D hydroxyderivatives, can provide a benefit for new or existing therapeutic approaches. We propose to target vitamin D signaling with the use of computational biology and AI tools to provide a solution to the melanoma problem.

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Section: Role Of Vitamin D In Melanomamentioning

confidence: 99%

Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling

Slominski,

Kim,

Janjetovic

et al. 2024

Cancers

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show abstract

“…18 Color illuminations also make a few challenges possessing its factors such as light rays, reflections, and color texture. 19 This paper develops an efficient automated and intelligent skin cancer detection system to detect and classify the cancer classes precisely. The following points present the key contributions of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The primary challenge in skin cancer is various shapes and sizes of images could not give the correct result of the detection 18 . Color illuminations also make a few challenges possessing its factors such as light rays, reflections, and color texture 19 . This paper develops an efficient automated and intelligent skin cancer detection system to detect and classify the cancer classes precisely.…”

Section: Introductionmentioning

confidence: 99%

An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model

Renith

Senthilselvi

2023

Int J Imaging Syst Tech

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Skin cancer is the most common deadly disease caused due to abnormal and uncontrolled growth of cells in the human body. According to a report, annually nearly one million people are affected by skin cancer worldwide. To protect human lives from such life‐threatening diseases, early identification of skin cancer is the only precautionary measure. In recent times, there already exist numerous automated techniques to detect and classify skin lesion malignancies using dermoscopic images. However, analyzing the dermoscopic images becomes an arduous task due to the presence of troublesome features such as light reflections, illumination variations, and uneven shape and dimension. To address the challenges faced during skin cancer recognition process, in this paper, we proposed an efficient intelligent automated system to detect and discriminate the dermoscopic images into malignant or benign. The proposed skin cancer detection model utilizes the HAM10000 dataset for evaluation. The dermoscopic images acquired from the HAM10000 dataset are initially preprocessed to enhance the quality of image and thus making it fit to train the classifier. Afterward, the most significant image patterns are extracted by the AlexNet architecture without any loss of detailed information. Later on, the extracted features are inputted to the proposed Improved Adaboost‐based Aphid–Ant Mutualism (IAB‐AAM) classification model to discriminate the images into malignant and benign categories. The proposed IAB‐AAM approach witnessed extensive enhancement in classification accuracy. The enhanced performance is attributed by integrating the AAM optimization concept with the IAB model. By comparing the performance of the proposed IAB‐AAM with other modern methods in terms of different evaluation indicators namely accuracy, precision, specificity, sensitivity, and f‐measure, the efficiency of the proposed IAB‐AAM technique is analyzed. From the experimental results, it is known that the proposed IAB‐AAM technique attains a greater accuracy rate of 95.7% in detecting skin cancer classes than other compared approaches.

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Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets

Lima

Krohling

2022

Intelligent Systems

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Artificial Intelligence in Skin Cancer: Diagnosis and Therapy

Cited by 5 publications

References 117 publications

Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling

Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling

An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model

Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets

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