Abstract
In the present world, skin cancer is the most widely recognized reason for death among people. Skin malignancy is an unusual development of skin cells. Frequently created on the body part exposed to the sunlight; however, it can happen on any place on the body. The majority of the skin malignancy is treatable at the beginning phase. So an early and quick identification of skin disease can spare the patient's life. With the new innovation, early identification of skin malignancy is conceivable at the introductory stage utilizing picture handling. The skin cancer detection using image processing is based on the detection of skin cancer types at its earliest stage. There are many types of skin cancers found. It is difficult to identify the type of skin cancer at the earlier stage, manual identification can often be time consuming and inaccurate. Doctors are able to identify the symptoms of skin cancer but are unable to identify the type of skin cancer in the initial stage. So the doctors will wait until it gets blotted but by that time the disease will become out of control. So a software is developed to help the Skin Cancer Detection at its earliest stage by passing valid input images. So this chapter explains about a method to identify and classify the skin cancer using images using Convolutional Neural Network (CNN) algorithm. The accuracy obtained by using the proposed method is 89%.
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Singh, C., Nischitha, Shetty, S.S., Bekal, A., Bhat, S., Badiger, M. (2024). Deep Learning Analysis for Skin Cancer Detection. In: Kalya, S., Kulkarni, M., Bhat, S. (eds) Advances in VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems. VSPICE 2022. Lecture Notes in Electrical Engineering, vol 1062. Springer, Singapore. https://doi.org/10.1007/978-981-99-4444-6_12
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DOI: https://doi.org/10.1007/978-981-99-4444-6_12
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