A Convolutional Deep Neural Network Approach for Skin Cancer Detection Using Skin Lesion Images
Malignant Melanoma, known simply as Melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient death. When detected early, Melanoma is curable. In this paper we propose a deep learning model (Convolutional Neural Networks) in order to automatically classify skin lesion images as Malignant or Benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.
[1] “What is melanoma skin cancer?” [Online].
Available: https://www.cancer.org/cancer/melanoma-skin-cancer/
about/what-is-melanoma.html
[2] M. MacGill, “What you should know about
melanoma,” Medical News Today, 2018. [Online]. Available:
https://www.medicalnewstoday.com/articles/154322.php
[3] F. Nachbar, W. Stolz, T. Merkle, A. B. Cognetta, T. Vogt, M. Landthaler,
P. Bilek, O. Braun-Falco, and G. Plewig, “The abcd rule of
dermatoscopy: high prospective value in the diagnosis of doubtful
melanocytic skin lesions,” Journal of the American Academy of
Dermatology, vol. 30, no. 4, pp. 551–559, 1994.
[4] G. Argenziano, G. Fabbrocini, P. Carli, V. De Giorgi, E. Sammarco, and
M. Delfino, “Epiluminescence microscopy for the diagnosis of doubtful
melanocytic skin lesions: comparison of the abcd rule of dermatoscopy
and a new 7-point checklist based on pattern analysis,” Archives of
dermatology, vol. 134, no. 12, pp. 1563–1570, 1998.
[5] P. Dubal, S. Bhatt, C. Joglekar, and S. Patii, “Skin cancer detection
and classification,” in 2017 6th international conference on electrical
engineering and informatics (ICEEI). IEEE, Conference Proceedings,
pp. 1–6.
[6] R. Moussa, F. Gerges, C. Salem, R. Akiki, O. Falou, and D. Azar,
“Computer-aided detection of melanoma using geometric features,”
in 2016 3rd Middle East Conference on Biomedical Engineering
(MECBME). IEEE, Conference Proceedings, pp. 125–128.
[7] S. Jain and N. Pise, “Computer aided melanoma skin cancer detection
using image processing,” Procedia Computer Science, vol. 48, pp.
735–740, 2015.
[8] A. Bhardwaj and J. Bhatia, “An image segmentation method for early
detection and analysis of melanoma,” IOSR Journal of Dental and
Medical Sciences, vol. 13, no. 10, pp. 18–22., 2014.
[9] N. F. M. Azmi, H. M. Sarkan, Y. Yahya, and S. Chuprat, “Abcd
rules segmentation on malignant tumor and benign skin lesion images,”
in 2016 3rd International Conference on Computer and Information
Sciences (ICCOINS). IEEE, Conference Proceedings, pp. 66–70.
[10] P. G. Cavalcanti, J. Scharcanski, and G. V. Baranoski, “A two-stage
approach for discriminating melanocytic skin lesions using standard
cameras,” Expert Systems with Applications, vol. 40, no. 10, pp.
4054–4064, 2013.
[11] X. Yuan, Z. Yang, G. Zouridakis, and N. Mullani, “Svm-based texture
classification and application to early melanoma detection,” in 2006
International Conference of the IEEE Engineering in Medicine and
Biology Society. IEEE, Conference Proceedings, pp. 4775–4778.
[12] J. L. G. Arroyo and B. G. Zapirain, “Detection of pigment network in
dermoscopy images using supervised machine learning and structural
analysis,” Computers in biology and medicine, vol. 44, pp. 144–157,
2014.
[13] C. Salem, D. Azar, and S. Tokajian, “An image processing and genetic
algorithm-based approach for the detection of melanoma in patients,”
Methods of information in medicine, vol. 57, no. 01/02, pp. 74–80, 2018.
[14] A. Agarwal, A. Issac, M. K. Dutta, K. Riha, and V. Uher,
“Automated skin lesion segmentation using k-means clustering from
digital dermoscopic images,” in 2017 40th International Conference on
Telecommunications and Signal Processing (TSP). IEEE, Conference
Proceedings, pp. 743–748.
[15] R. Collobert and J. Weston, “A unified architecture for natural
language processing: Deep neural networks with multitask learning,” in
Proceedings of the 25th international conference on Machine learning.
ACM, Conference Proceedings, pp. 160–167.
[16] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image
recognition,” in Proceedings of the IEEE conference on computer vision
and pattern recognition, Conference Proceedings, pp. 770–778.
[17] G. Hinton, L. Deng, D. Yu, G. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior,
V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks
for acoustic modeling in speech recognition,” IEEE Signal processing
magazine, vol. 29, 2012.
[18] E. Nasr-Esfahani, S. Samavi, N. Karimi, S. M. R. Soroushmehr, M. H.
Jafari, K. Ward, and K. Najarian, “Melanoma detection by analysis
of clinical images using convolutional neural network,” in 2016 38th
Annual International Conference of the IEEE Engineering in Medicine
and Biology Society (EMBC). IEEE, Conference Proceedings, pp.
1373–1376.
[19] E. Zagrouba and W. Barhoumi, “A prelimary approach for the automated
recognition of malignant melanoma,” Image Analysis and Stereology,
vol. 23, no. 2, pp. 121–135, 2011.
[20] C. Munteanu and S. Cooclea, “Spotmole—melanoma control system,”
2009.
[21] I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman, and N. Petkov,
“Med-node: a computer-assisted melanoma diagnosis system using
non-dermoscopic images,” Expert systems with applications, vol. 42,
no. 19, pp. 6578–6585, 2015.
[22] L. Yu, H. Chen, Q. Dou, J. Qin, and P.-A. Heng, “Automated melanoma
recognition in dermoscopy images via very deep residual networks,”
IEEE transactions on medical imaging, vol. 36, no. 4, pp. 994–1004,
2016.
[23] A. Menegola, M. Fornaciali, R. Pires, F. V. Bittencourt, S. Avila,
and E. Valle, “Knowledge transfer for melanoma screening with deep
learning,” in 2017 IEEE 14th International Symposium on Biomedical
Imaging (ISBI 2017). IEEE, Conference Proceedings, pp. 297–300.
[24] L. Torrey and J. Shavlik, Transfer learning. IGI Global, 2010, pp.
242–264.
[25] J. Kawahara, A. BenTaieb, and G. Hamarneh, “Deep features to
classify skin lesions,” in 2016 IEEE 13th International Symposium
on Biomedical Imaging (ISBI). IEEE, Conference Proceedings, pp.
1397–1400.
[26] N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, and J. R. Smith,
“Deep learning, sparse coding, and svm for melanoma recognition in
dermoscopy images,” in International Workshop on Machine Learning
in Medical Imaging. Springer, Conference Proceedings, pp. 118–126.
[27] M. H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, S. M. R.
Soroushmehr, K. Ward, and K. Najarian, “Skin lesion segmentation
in clinical images using deep learning,” in 2016 23rd International
conference on pattern recognition (ICPR). IEEE, Conference
Proceedings, pp. 337–342.
[28] Y. Yuan, M. Chao, and Y.-C. Lo, “Automatic skin lesion segmentation
using deep fully convolutional networks with jaccard distance,” IEEE
transactions on medical imaging, vol. 36, no. 9, pp. 1876–1886, 2017.
[29] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press,
2016.
[1] “What is melanoma skin cancer?” [Online].
Available: https://www.cancer.org/cancer/melanoma-skin-cancer/
about/what-is-melanoma.html
[2] M. MacGill, “What you should know about
melanoma,” Medical News Today, 2018. [Online]. Available:
https://www.medicalnewstoday.com/articles/154322.php
[3] F. Nachbar, W. Stolz, T. Merkle, A. B. Cognetta, T. Vogt, M. Landthaler,
P. Bilek, O. Braun-Falco, and G. Plewig, “The abcd rule of
dermatoscopy: high prospective value in the diagnosis of doubtful
melanocytic skin lesions,” Journal of the American Academy of
Dermatology, vol. 30, no. 4, pp. 551–559, 1994.
[4] G. Argenziano, G. Fabbrocini, P. Carli, V. De Giorgi, E. Sammarco, and
M. Delfino, “Epiluminescence microscopy for the diagnosis of doubtful
melanocytic skin lesions: comparison of the abcd rule of dermatoscopy
and a new 7-point checklist based on pattern analysis,” Archives of
dermatology, vol. 134, no. 12, pp. 1563–1570, 1998.
[5] P. Dubal, S. Bhatt, C. Joglekar, and S. Patii, “Skin cancer detection
and classification,” in 2017 6th international conference on electrical
engineering and informatics (ICEEI). IEEE, Conference Proceedings,
pp. 1–6.
[6] R. Moussa, F. Gerges, C. Salem, R. Akiki, O. Falou, and D. Azar,
“Computer-aided detection of melanoma using geometric features,”
in 2016 3rd Middle East Conference on Biomedical Engineering
(MECBME). IEEE, Conference Proceedings, pp. 125–128.
[7] S. Jain and N. Pise, “Computer aided melanoma skin cancer detection
using image processing,” Procedia Computer Science, vol. 48, pp.
735–740, 2015.
[8] A. Bhardwaj and J. Bhatia, “An image segmentation method for early
detection and analysis of melanoma,” IOSR Journal of Dental and
Medical Sciences, vol. 13, no. 10, pp. 18–22., 2014.
[9] N. F. M. Azmi, H. M. Sarkan, Y. Yahya, and S. Chuprat, “Abcd
rules segmentation on malignant tumor and benign skin lesion images,”
in 2016 3rd International Conference on Computer and Information
Sciences (ICCOINS). IEEE, Conference Proceedings, pp. 66–70.
[10] P. G. Cavalcanti, J. Scharcanski, and G. V. Baranoski, “A two-stage
approach for discriminating melanocytic skin lesions using standard
cameras,” Expert Systems with Applications, vol. 40, no. 10, pp.
4054–4064, 2013.
[11] X. Yuan, Z. Yang, G. Zouridakis, and N. Mullani, “Svm-based texture
classification and application to early melanoma detection,” in 2006
International Conference of the IEEE Engineering in Medicine and
Biology Society. IEEE, Conference Proceedings, pp. 4775–4778.
[12] J. L. G. Arroyo and B. G. Zapirain, “Detection of pigment network in
dermoscopy images using supervised machine learning and structural
analysis,” Computers in biology and medicine, vol. 44, pp. 144–157,
2014.
[13] C. Salem, D. Azar, and S. Tokajian, “An image processing and genetic
algorithm-based approach for the detection of melanoma in patients,”
Methods of information in medicine, vol. 57, no. 01/02, pp. 74–80, 2018.
[14] A. Agarwal, A. Issac, M. K. Dutta, K. Riha, and V. Uher,
“Automated skin lesion segmentation using k-means clustering from
digital dermoscopic images,” in 2017 40th International Conference on
Telecommunications and Signal Processing (TSP). IEEE, Conference
Proceedings, pp. 743–748.
[15] R. Collobert and J. Weston, “A unified architecture for natural
language processing: Deep neural networks with multitask learning,” in
Proceedings of the 25th international conference on Machine learning.
ACM, Conference Proceedings, pp. 160–167.
[16] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image
recognition,” in Proceedings of the IEEE conference on computer vision
and pattern recognition, Conference Proceedings, pp. 770–778.
[17] G. Hinton, L. Deng, D. Yu, G. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior,
V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks
for acoustic modeling in speech recognition,” IEEE Signal processing
magazine, vol. 29, 2012.
[18] E. Nasr-Esfahani, S. Samavi, N. Karimi, S. M. R. Soroushmehr, M. H.
Jafari, K. Ward, and K. Najarian, “Melanoma detection by analysis
of clinical images using convolutional neural network,” in 2016 38th
Annual International Conference of the IEEE Engineering in Medicine
and Biology Society (EMBC). IEEE, Conference Proceedings, pp.
1373–1376.
[19] E. Zagrouba and W. Barhoumi, “A prelimary approach for the automated
recognition of malignant melanoma,” Image Analysis and Stereology,
vol. 23, no. 2, pp. 121–135, 2011.
[20] C. Munteanu and S. Cooclea, “Spotmole—melanoma control system,”
2009.
[21] I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman, and N. Petkov,
“Med-node: a computer-assisted melanoma diagnosis system using
non-dermoscopic images,” Expert systems with applications, vol. 42,
no. 19, pp. 6578–6585, 2015.
[22] L. Yu, H. Chen, Q. Dou, J. Qin, and P.-A. Heng, “Automated melanoma
recognition in dermoscopy images via very deep residual networks,”
IEEE transactions on medical imaging, vol. 36, no. 4, pp. 994–1004,
2016.
[23] A. Menegola, M. Fornaciali, R. Pires, F. V. Bittencourt, S. Avila,
and E. Valle, “Knowledge transfer for melanoma screening with deep
learning,” in 2017 IEEE 14th International Symposium on Biomedical
Imaging (ISBI 2017). IEEE, Conference Proceedings, pp. 297–300.
[24] L. Torrey and J. Shavlik, Transfer learning. IGI Global, 2010, pp.
242–264.
[25] J. Kawahara, A. BenTaieb, and G. Hamarneh, “Deep features to
classify skin lesions,” in 2016 IEEE 13th International Symposium
on Biomedical Imaging (ISBI). IEEE, Conference Proceedings, pp.
1397–1400.
[26] N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, and J. R. Smith,
“Deep learning, sparse coding, and svm for melanoma recognition in
dermoscopy images,” in International Workshop on Machine Learning
in Medical Imaging. Springer, Conference Proceedings, pp. 118–126.
[27] M. H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, S. M. R.
Soroushmehr, K. Ward, and K. Najarian, “Skin lesion segmentation
in clinical images using deep learning,” in 2016 23rd International
conference on pattern recognition (ICPR). IEEE, Conference
Proceedings, pp. 337–342.
[28] Y. Yuan, M. Chao, and Y.-C. Lo, “Automatic skin lesion segmentation
using deep fully convolutional networks with jaccard distance,” IEEE
transactions on medical imaging, vol. 36, no. 9, pp. 1876–1886, 2017.
[29] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press,
2016.
@article{"International Journal of Information, Control and Computer Sciences:80440", author = "Firas Gerges and Frank Y. Shih", title = "A Convolutional Deep Neural Network Approach for Skin Cancer Detection Using Skin Lesion Images", abstract = "Malignant Melanoma, known simply as Melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient death. When detected early, Melanoma is curable. In this paper we propose a deep learning model (Convolutional Neural Networks) in order to automatically classify skin lesion images as Malignant or Benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.", keywords = "Deep learning, skin cancer, image processing,
melanoma.", volume = "15", number = "8", pages = "335-4", }
