Open AccessClassification of skin cancer images by applying simple evolving connectionist system
Author(s) -
Al-Khowarizmi Al-Khowarizmi,
Suherman Suherman
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i2.pp421-429
Subject(s) - computer science , connectionism , artificial intelligence , word error rate , artificial neural network , pattern recognition (psychology) , skin cancer , span (engineering) , feed forward , deep learning , euclidean distance , cancer , medicine , civil engineering , engineering , control engineering
Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.