Melanoma Skin Cancer Classification Using Transfer Learning

Pillay, Verosha; Hirasen, Divyan; Viriri, Serestina; Gwetu, Mandlenkosi Victor

doi:10.1007/978-3-030-63119-2_24

Cited by 6 publications

(1 citation statement)

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“…In the United States, 5.4 million new cases of skin cancer are detected each year. Melanomas account for fewer than 5% of all skin malignancies in the United States, yet they are responsible for more than 75% of all skin cancer-related mortality, with over 10,000 fatalities each year in the United States alone [4]. One out of every five Americans will be diagnosed with cutaneous cancer over their lifetime.…”

Section: Introductionmentioning

confidence: 99%

A combined approach of VGG 16 and LSTM transfer learning technique for skin melanoma classification

Arjun

Kumar

2022

ijhs

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To distinguish melanoma from other skin illnesses, doctors examine pigmented lesions on the skin. Damaged DNA causes cells to expand uncontrollably, and the rate of growth is currently increasing rapidly. Melanoma is a kind of skin cancer induced by UV radiation from the sun, with a survival rate of about 15-20 percent. Increased UV light on the earth's surface is also aiding the spread of skin cancer throughout the globe. Melanoma is diagnosed late, resulting in severe malignancy and spread to other bodily organs such as the liver, lungs, and brain. Melanoma diagnosis from dermoscopic skin samples automatically is a difficult problem. The purpose of Computer Vision, Machine Learning, and Deep Learning in the era of digital pictures is to extract information from them and develop new knowledge. Deep convolutional neural network (DCNN) models have been extensively studied for skin disease detection, with some achieving diagnostic results that are equivalent to or even better than dermatologists. Pre-processing involves first applying a filter or kernel to reduce noise and artefacts, then trained on a variety of tiny, unbalanced datasets to ensure that the moderately complicated models outperform the bigger models. Finally, to minimise overfitting, regularization DropOut is introduced.

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Section: Introductionmentioning

confidence: 99%