1 research outputs found
Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network
Melanoma skin cancer is one of the most dangerous and life-threatening
cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which
causes melanoma skin cancer. However, it is difficult to detect and classify
melanoma and nevus mole at the immature stages. In this work, an automatic deep
learning system is developed based on the intensity value estimation with a
convolutional neural network model (CNN) to detect and classify melanoma and
nevus mole more accurately. Since intensity levels are the most distinctive
features for object or region of interest identification, the high-intensity
pixel values are selected from the extracted lesion images. Incorporating those
high-intensity features into the CNN improves the overall performance of the
proposed model than the state-of-the-art methods for detecting melanoma skin
cancer. To evaluate the system, we used 5-fold cross-validation. Experimental
results show that a superior percentage of accuracy (92.58%), sensitivity
(93.76%), specificity (91.56%), and precision (90.68%) are achieved.Comment: The paper has been accepted for publication in Computer Science
journal: http://journals.agh.edu.pl/csc