@ARTICLE{Mohamed_Abeer_Deep_2019,
 author={Mohamed, Abeer and Mohamed, Wael A. and Zekry, Abdel Halim},
 volume={vol. 65},
 number={No 3},
 journal={International Journal of Electronics and Telecommunications},
 pages={507-512},
 howpublished={online},
 year={2019},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.},
 type={Artykuły / Articles},
 title={Deep Learning Can Improve Early Skin Cancer Detection},
 URL={http://journals.pan.pl/Content/113310/PDF/68.pdf},
 doi={10.24425/ijet.2019.129806},
 keywords={technology, dermoscopic lesions, convolutional neural network, ISIC dataset, deep learning, neural networks},
}