Skin Cancer Identification and Classification using Lenet Convolution Neural Network

Resumen

The abnormal development of skin cells within a human body is known as skin cancer.
Unusual changes in your skin can indicate the presence of certain malignancies. Being on the
lookout for abnormalities in your skin might help you obtain a diagnosis quickly. The primary
attributes of each image are based on shape and texture-oriented elements, and samples of
images containing different types of skin cancer are collected. It describes a cutting-edge
method that combines rapid disease diagnosis and that of deep learning by using convolutional
neural networks (CNNs), which have been proven to be effective against categorizing the
various skin cancer types. Using CNN and publicly available skin cancer pathology image
datasets, a number of neuron-wise and layer-wise visualisation techniques are implemented.
Our proposed algorithm utilises neural networks to record the texture and colour of lesions
pertaining to different types of skin cancers during diagnosis, comparable to how people make
decisions. The Django web framework will then be used to deploy this model.