Machine Learning Techniques for Automated Melanoma Detection

Vocaturo, Eugenio; Perna, Diego; Zumpano, Ester

doi:10.1109/bibm47256.2019.8983165

Cited by 30 publications

(13 citation statements)

References 61 publications

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“…The use of AI in medicine is continuously growing and, if embraced by clinicians, can strongly augment clinical decision-making and also increase access to care [23,24]. The integration of AI and machine learning in melanoma detection has the strong potential to foster a more ordered, quantitative, and non-invasive approach to recognizing and diagnosing atypical lesions [24,25].…”

Section: Digital Dermoscopy Ai and Machine Learningmentioning

confidence: 99%

Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on Light-Scattering Properties

Silver

Deshmukh²,

Nadiminti³

et al. 2023

Life

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Cutaneous melanoma is a cancer with metastatic potential characterized by varying amounts of pigment-producing melanocytes, and it is one of the most aggressive and fatal forms of skin malignancy, with several hundreds of thousands of cases each year. Early detection and therapy can lead to decreased morbidity and decreased cost of therapy. In the clinic, this often translates to annual skin screenings, especially for high-risk patients, and generous use of the ABCDE (asymmetry, border irregularity, color, diameter, evolving) criteria. We have used a new technique termed vibrational optical coherence tomography (VOCT) to non-invasively differentiate between pigmented and non-pigmented melanomas in a pilot study. The VOCT results reported in this study indicate that both pigmented and non-pigmented melanomas have similar characteristics, including new 80, 130, and 250 Hz peaks. Pigmented melanomas have larger 80 Hz peaks and smaller 250 Hz peaks than non-pigmented cancers. The 80 and 250 Hz peaks can be used to quantitative characterize differences between different melanomas. In addition, infrared light penetration depths indicated that melanin in pigmented melanomas has higher packing densities than in non-pigmented lesions. Using machine learning techniques, the sensitivity and specificity of differentiating skin cancers from normal skin are shown to range from about 78% to over 90% in this pilot study. It is proposed that using AI on both lesion histopathology and mechanovibrational peak heights may provide even higher specificity and sensitivity for differentiating the metastatic potential of different melanocytic lesions.

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Section: Digital Dermoscopy Ai and Machine Learningmentioning

confidence: 99%

Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on Light-Scattering Properties

Silver

Deshmukh²,

Nadiminti³

et al. 2023

Life

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“…For illustration, ABCD rules, pattern generation, seven-point checklist, and utilizing well-developed classifiers like SVM are confined to dermoscopy or histopathology picture information [14] [15]. There are some researcher detect melanoma using machine learning algorithm [17] where some discuss about how to detect melanoma using machine learning [16]. Adekanmi Adegun and Serestina Viriri [23] discussed traditional-based classification techniques and the recently developed state-of-the-art techniques for Melanoma detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Eliminating racial bias at the time of detection Melanoma using Convolution Neural Network (CNN)

Majumder¹,

Taief²,

Bhuiyan³

et al. 2021

SCRS Conference Proceedings on Intelligent Systems

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Melanoma considers deadly cancer that can cause the death of a person if not distinguished at an initial stage. Although Melanoma is most common in white skin people and can be detected at an early stage using AI, white skin people have a much lower death rate from this cancer. But when black skin people have Melanoma, AI can't detect it at an early stage because most of the time, the machines are trained with Dermoscopic pictures of white people, which leads to a higher mortality rate for black skin people. As a result, people don't want to trust the AI system at the time of Melanoma detection. In this paper, we proposed a model with whatever black or white skin it can easily detect using machine learning. In this case, we will use the Convolution Neural Network (CNN) of machine learning to detect Melanoma at an early stage so that the death rate caused by Melanoma cancer can be reduced. The proposed method can detect Melanoma with an accuracy of 88.9\% for both skin people which may significantly decrease the mortality rate.

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“…Acral lentiginous melanoma appears as a tiny (about 6 mm) flat spot of discolored skin, often black or dark brown. It usually grows on the soles, palms, or under nails sometimes and mainly occurs on the back of men and fingers and legs in women [4]. It has poor diagnosis because it is hard to differentiate between an acral melanoma and an acral nevus.…”

Section: Introductionmentioning

confidence: 99%

Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks

et al. 2021

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Human skin is the most exposed part of the human body that needs constant protection and care from heat, light, dust, and direct exposure to other harmful radiation, such as UV rays. Skin cancer is one of the dangerous diseases found in humans. Melanoma is a form of skin cancer that begins in the cells (melanocytes) that control the pigment in human skin. Early detection and diagnosis of skin cancer, such as melanoma, is necessary to reduce the death rate due to skin cancer. In this paper, the classification of acral lentiginous melanoma, a type of melanoma with benign nevi, is being carried out. The proposed stacked ensemble method for melanoma classification uses different pre-trained models, such as Xception, Inceptionv3, InceptionResNet-V2, DenseNet121, and DenseNet201, by employing the concept of transfer learning and fine-tuning. The selection of pre-trained CNN architectures for transfer learning is based on models having the highest top-1 and top-5 accuracies on ImageNet. A novel stacked ensemble-based framework is presented to improve the generalizability and increase robustness by fusing fine-tuned pre-trained CNN models for acral lentiginous melanoma classification. The performance of the proposed method is evaluated by experimenting on a Figshare benchmark dataset. The impact of applying different augmentation techniques has also been analyzed through extensive experimentations. The results confirm that the proposed method outperforms state-of-the-art techniques and achieves an accuracy of 97.93%.

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Machine Learning Techniques for Automated Melanoma Detection

Cited by 30 publications

References 61 publications

Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on Light-Scattering Properties

Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on Light-Scattering Properties

Eliminating racial bias at the time of detection Melanoma using Convolution Neural Network (CNN)

Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks

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