Open AccessA Deep CNN Model for Skin Cancer Detection and Classification
Author(s) -
Masum Shah Junayed,
Nipa Anjum,
Abu Noman Sakib,
Md Baharul Islam
Publication year - 2021
Publication title -
computer science research notes
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.11
H-Index - 4
eISSN - 2464-4625
pISSN - 2464-4617
DOI - 10.24132/csrn.2021.3002.8
Subject(s) - deep learning , convolutional neural network , artificial intelligence , computer science , skin cancer , pattern recognition (psychology) , convolution (computer science) , process (computing) , cancer , artificial neural network , machine learning , cancer detection , medicine , operating system
Skin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection ofskin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-basedmethods help dermatologists in classifying medical images. This paper proposes a deep learning-based modelto detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, wecollected a dataset that includes four skin cancer image data before applying them in augmentation techniques toincrease the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data,our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNetby 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while beingcomputationally comparable.