Diagnosis of skin cancer using deep learning

Sweta Sweta; Nitin Mishra; Saumya Chaturvedi

doi:10.53730/ijhs.v6nS5.9368

Authors

Sweta Galgotias University, Galgotias University, Sharda University
Nitin Mishra Galgotias University, Galgotias University, Sharda University
Saumya Chaturvedi Galgotias University, Galgotias University, Sharda University

Keywords:

Dermatitis, Melanoma, Deep Neural Network, Convolution Neural Network, Transfer Learning

Abstract

Skin diseases consists a wide range of ailments that affect the skin, including microbial infections, viral, fungal, allergies, epidermis malignancies, and parasitic diseases. In South-Asian countries like India people don’t care much about the skin conditions. In our country, people prefer home remedies to cure skin conditions instead of visiting a dermatologist which can lead to serious skin conditions. Early diagnosis of skin disease is very important as it can reduce the severity of the condition. Melanoma is the deadliest type of skin cancer, and it is the most prominent form of cancer. Melanoma could be diagnosed early, which would reduce overall illness and death. The odds of dying from the ailment is proportional to the extent of the malignancy, which is proportional to the length of time it has been growing. The keys to early detection are patient self-examination of the skin, full-body skin screenings by a dermatologist, and patient engagement. This work aims to categorize skin cancer into two types: malignant and benign. Two different approaches were used. Starting with a simple Convolutional Neural Network and then moving on to transfer learning. In our experiment, we were able to attain a classification accuracy of 82 percent.

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