Skin Cancer Detection using Python Machine learning Project Report

Skin Cancer Detection using Python Machine learning Project Report

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Artificial intelligence (AI) has wide applications in healthcare, including dermatology.
Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can
progressively learn from data to predict the characteristics of new samples and perform a
desired task. Although it has a significant role in the detection of skin cancer, dermatology skill
lags behind radiology in terms of AI acceptance. With continuous spread, use, and emerging
technologies, AI is becoming more widely available even to the general population. AI can be
of use for the early detection of skin cancer. For example, the use of deep convolutional neural
networks can help to develop a system to evaluate images of the skin to diagnose skin cancer.
Early detection is key for the effective treatment and better outcomes of skin cancer. Specialists
can accurately diagnose the cancer, however, considering their limited numbers, there is a need
to develop automated systems that can diagnose the disease efficiently to save lives and reduce
health and financial burdens on the patients. ML can be of significant use in this regard. In this
article, we discuss the fundamentals of ML and its potential in assisting the diagnosis of skin