Trlu: A Customized Activation Function to Detect Erythemato-Squamous Skin Cancer at Early Stage

Main Article Content

Rajashekar Deva, Dr. G .Narsimha


The life risk factor for the cancer disease is becoming more and more in the present living life style. Many researchers focused on skin cancer using machine learning approaches, but when working with multi classification data, it is observed that ANN gives better results, if the input data is keeps on adding in the real time scenario. This proposed system focuses on the classification of the skin cancer types by designing the neural network based on the customized activation function. Many parameters are involved in designing a suitable classifier using NN like learning rate, optimizer, activator, and other normalization layers. This paper majorly focuses on the activation function and number of neurons associated with the layer because these two parameters play a vital role in the entire accuracy of the model. Among the existing activators, the combination of tanh and relu has given high value base on them; a new activation function is designed

Article Details