Abstract
Melanoma is a specific type of skin cancer that can be lethal if not diagnosed and treated early. This paper presents a deep-learning approach for the automatic identification of melanoma on dermoscopic images from the ISIC Archive dataset and non-dermoscopic images from the MED-NODE dataset. The method involves the development of Convolutional Neural Network (CNN) and ResNet50 models, along with various pre-processing techniques. The CNN and ResNet50 models detect melanoma from dermoscopic images with 98.07% and 99.83% accuracy respectively, using hair removal and augmentation techniques. For non-dermoscopic images, the CNN and ResNet50 models achieve an accuracy of 97.06% and 100% respectively, using the hair removal technique. Furthermore, combining age and gender as additional factors in identifying melanoma in dermoscopic images, leads to an accuracy of 96.40% using CNN. The results of this research suggest that the developed models when combined with various pre-processing techniques and the integration of age and gender as additional factors, can be an efficient tool in the early detection of melanoma.
References
Skin cancer - Symptoms and causes. (2022, December 6). Mayo Clinic.https://www.mayoclinic.org/diseases-conditions/skin-cancer/symptoms-causes/syc-20377605
P. B. C. Castro, B. Krohling, A. G. C. Pacheco and R. A. Krohling, "An app to detect melanoma using deep learning: An approach to handle imbalanced data based on evolutionary algorithms," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-6, doi: 10.1109/IJCNN48605.2020.9207552.
B. Sreedhar, M. Swamy B.E, and M. S. Kumar, "A Comparative Study of Melanoma Skin Cancer Detection in Traditional and Current Image Processing Techniques," 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics, and Cloud) (I-SMAC), Palladam, India, 2020, pp. 654-658, doi: 10.1109/I-SMAC49090.2020.9243501.
J. Fernandez Alcon et al., "Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis," in IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 1, pp. 14-25, Feb. 2009, doi: 10.1109/JSTSP.2008.2011156.
E. Jana, R. Subban, and S. Saraswathi, "Research on Skin Cancer Cell Detection Using Image Processing," 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 2017, pp. 1-8, doi: 10.1109/ICCIC.2017.8524554.
S. R. Guha and S. M. Rafizul Haque, "Convolutional Neural Network Based Skin Lesion Analysis for Classifying Melanoma," 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, 2019, pp. 1-5, doi: 10.1109/STI47673.2019.9067979.
T. Alkarakatly, S. Eidhah, M. Al-Sarawani, A. Al-Sobhi, and M. Bilal, "Skin Lesions Identification Using Deep Convolutional Neural Network," 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), Al Madinah Al Munawwarah, Saudi Arabia, 2020, pp. 1-5, doi: 10.1109/AECT47998.2020.9194205.
H. K. Kondaveeti and P. Edupuganti, "Skin Cancer Classification using Transfer Learning," 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI), Buldhana, India, 2020, pp. 1-4, doi: 10.1109/ICATMRI51801.2020.9398388.
Shalu and A. Kamboj, "A Color-Based Approach for Melanoma Skin Cancer Detection," 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, 2018, pp. 508-513, doi: 10.1109/ICSCCC.2018.8703309.
F. Claudio, Kaggle, “ Skin Cancer: Malignant vs. Benign," 2018[Online].Available:https://www.kaggle.com/datasets/fanconic/skin-cancer-malignant-vs-benign
MED-NODE dataset 2015[Online].Available: https://www.cs.rug.nl/~imaging/databases/melanoma_naevi/
A. Naeem, M. S. Farooq, A. Khelifi and A. Abid, "Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges, and Opportunities," in IEEE Access, vol. 8, pp. 110575-110597, 2020, doi: 10.1109/ACCESS.2020.300150
S. Mane and S. Shinde, "A Method for Melanoma Skin Cancer Detection Using Dermoscopy Images," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2018, pp. 1-6, doi: 10.1109/ICCUBEA.2018.8697804.
M. A. Hossin, F. F. Rupom, H. R. Mahi, A. Sarker, F. Ahsan and S. Warech, "Melanoma Skin Cancer Detection Using Deep Learning and Advanced Regularizer," 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia, 2020, pp. 89-94, doi: 10.1109/ICACSIS51025.2020.9263118.
M. M. I. Rahi, F. T. Khan, M. T. Mahtab, A. K. M. Amanat Ullah, M. G. R. Alam, and M. A. Alam, "Detection Of Skin Cancer Using Deep Neural Networks," 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Melbourne, VIC, Australia, 2019, pp. 1-7, doi: 10.1109/CSDE48274.2019.9162400.