Melanoma Skin Cancer Detection Using Different Machine Learning Technique
Keywords:
Melanoma, Dermatoscope, CNN, SVM, Preprocessing, Diagnosis, Feature Extraction, skin lesion identification, segmentation, ABCDE, handcrafted, color, texture feature, classifierAbstract
Expanded rate of skin cancer is vast. Melanoma is one of the most increased cancers since past decades. It should be detected early because of its aggressiveness. To diagnose melanoma earlier, skin lesion should be segmented correctly and characterize the benign and malignant cases. In this study, we combine handcrafted and automatic feature of CNN to generate the high classification accuracy with all CNN layers and combining integrated approach of CNN, sparse coding and Neural Network and K-Nearest Neighbor to identify melanoma and classify and plot it using principal component analysis algorithm and classified the melanoma skin lesion and train and test the skin lesion image and evaluate the skin lesion sensitivity, specificity, accuracy and generate the ROC Curve. The outcome of tentative evaluation proves that using ISIC, ISBI, VGG database to achieve 97% accuracy, 96% specificity and 95% sensitivity.
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Abbasi, R. N. (n.d.). Early diagnosis of cutaneous melanoma - revisiting the ABCD criteria. The Journal of American Medical.
M. Binder, M. Schwarz, A. Winkler, A. Steiner, A. Kaider, K. Wolff, and H. Pehamberger, “Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists,” vol. 131, no. 3, pp. 286-291, 1995.
M. E. Celebi, Q. Wen, H. Iyatomi, K. Shimizu, H. Zhou, and G. Schaefer, A state- of-the-art survey on lesion border detection in dermoscopy images, pp. 97-129: CRC Press, 2015.
F. Bogo, F. Peruch, A. Fortina, and E. Peserico, Where’s the lesion?: variability in human and automated segmentation of dermoscopy images of melanocytic skin lesions, pp. 67-96: CRC Press, 2015.
Erkol, B.; Moss, R.H.; Stanley, R.J.; Stoecker, W.V.; Hvatum, E. Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Res. Technol. 2005, 11, 17–26.
Gómez, D.D.; Butakoff, C.; Ersbøll, B.K.; Stoecker,W. Independent histogram pursuit for segmentation of skin lesions. IEEE Trans. Biomed. Eng. 2008, 55, 157–161.
Dhawan, A. P., & Sim, A. (1992). Segmentation of images of skin lesions using color and texture information of surface pigmentation. Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society, 16(3), 163–177. doi:10.1016/0895-6111(92)90071-g
Abuzaghleh, O., Barkana, B.D., Faezipour, M.: Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE J. Transl. Eng. Health Med. 3, 1–12 (2015).
Zhou, H., Schaefer, G., Celebi, M. E., Iyatomi, H., Norton, K., Liu, T., & Lin, F. (2010, August). Skin lesion segmentation using an improved snake model. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Presented at the 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010), Buenos Aires. doi:10.1109/iembs.2010.5627556
Qaisar, A., Celebi, M. E., Carmen, S., Fondón, G. I., & Ma, G. (2013). Pattern classification of dermoscopy images: a perceptually uniform model. Pattern Recogn, 46, 86–97.
Delgado, D., Butakoff, C., Ersboll, B. K., & Stoecker, W. (2008). Independent histogram pursuit for segmentation of skin lesions. IEEE Transactions on Biomedical Engineering, 55, 157–161.
Celebi, M. E., Kingravi, H. A., Iyatomi, H., Lee, J., Aslandogan, Y. A., Van Stoecker, W., … Marghoob, A. A. (2007, March 8). Fast and accurate border detection in dermoscopy images using statistical region merging. In J. P. W. Pluim & J. M. Reinhardt (Eds.), Medical Imaging 2007: Image Processing. doi:10.1117/12.709073
Celebi, M., Kingravi, H., Iyatomi, H., Aslandogan, Y., Stoecker, W., & Moss, R. (2008).’ Border detection in dermoscopy images using statistical region merging. Journal of Skin Research and Technology.
Sadeghi, M., Lee, T. K., Mclean, D., Lui, H., & Atkins, S. (2013). ’ Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions. IEEE Transactions on Medical Imaging, 32(5).
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017).
Almansour, E., & Jaffar, M. A. (2016). Classification of Dermoscopic Skin Cancer Images Using Color and Hybrid Texture Features. IJCSNS Int J Comput Sci Netw Secur, 16(4), 135–139.
Argenziano, G., Fabbrocini, G., Carli, P., Giorgi, V. D., Sammarco, E., & Delfino, M. (1998). 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 Dermatological Research, 134(12), 1563–1570.
Nachbar, F., Stolz, W., Merckle, T., Cognett A, A. B., Vogt, T., Landthaler, M., … Plewig, G. (1994). The ABCD rule of dermatoscopy: High prospective value in the diagnosis of doubtful melanocytic skin lesions. Journal of American Academy of Dermotology, 30, 551–559.
Barata, C., Emre Celebi, M., & Marques, J. S. (2015, August). Melanoma detection algorithm based on feature fusion. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Presented at the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan. doi:10.1109/embc.2015.7318937
Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., & Smith, J. R. (2015). Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In Lecture Notes in Computer Science. Machine Learning in Medical Imaging (pp. 118–126). doi:10.1007/978-3-319-24888-2_15
Abbes, W., & Sellami, D. (2016). High-level features for automatic skin lesions neural net-work based classification. In 2016 International Image Processing, Applications and Systems (IPAS). pp. 17.
Ahlberg, C., Williamson, C., & Shneiderman, B. (1992). Dynamic queries for information exploration. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI ’92. Presented at the the SIGCHI conference, Monterey, California, United States. doi:10.1145/142750.143054
Bakheet, S. (2017). An SVM framework for malignant melanoma detection based on optimized HOG features. Computation (Basel, Switzerland), 5(1), 4. doi:10.3390/computation5010004
Tian, P. (2013). ’A Review on Image Feature Extraction and Representation Techniques”. International Journal of Multimedia and Ubiquitous, 8, 385–396.
E. Alaa, and H. Demirel, “Co- occurrence matrix and its statistical features as a new approach for face recognition.”
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., … Halpern, A. (2018, April). Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Presented at the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC. doi:10.1109/isbi.2018.8363547
Arroyo, J.L.G., Zapirain, B.G.: ‘Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis’, Comput. Biol. Med., 2014, 44, pp. 144–157
Capdehourat, G., Corez, A., Bazzano, A., Alonso, R., & Musé, P. (2011). Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions. Pattern Recognition Letters, 32(16), 2187–2196. doi:10.1016/j.patrec.2011.06.015
Clawson, K. M., Morrow, P., & Scotney, B. (2009). Analysis of pigmented skin lesion border irregularity using the harmonic wavelet transform’. 13th Int. Machine Vision and Image Processing Conf. 18–23.
He, K., Zhang, X., Ren, S., & Sun, J. (2016, June). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. doi:10.1109/cvpr.2016.90
Arevalo, J., González, F. A., Ramos-Pollán, R., Oliveira, J. L., & Guevara Lopez, M. A. (2016). Representation learning for mammography mass lesion classification with convolutional neural networks. Computer Methods and Programs in Biomedicine, 127, 248–257. doi:10.1016/j.cmpb.2015.12.014
Barata, C., Celebi, M. E., & Marques, J. S. (2015). Improving dermoscopy image classification using color constancy. IEEE Journal of Biomedical and Health Informatics, 19(3), 1146–1152. doi:10.1109/JBHI.2014.2336473
Abuzaghleh, O., Barkana, B. D., & Faezipour, M. (2014, May). Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention. IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014. Presented at the 2014 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, USA. doi:10.1109/lisat.2014.6845199
Korotkov, K., Garcia, R.: ‘Computerized analysis of pigmented skin lesions: a review’, Artif. Intell. Med., 2012, 56, (2), pp. 69–90
Capdehourat, G., Corez, A., Bazzano, A., et al.: ‘Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions’, Pattern Recognit. Lett., 2011, 32, (16), pp. 2187– 2196
Yuan, X., Yang, Z., Zouridakis, G., & Mullani, N. (n.d.). SVM-based Texture Classification and Application to Early Melanoma Detection”, Proceedings of the 28th IEEE EMBS Annual International Conference. New York City, USA.
M. J. Ogorzaek, G. Surówka, L. Nowak, C. Merkwirth,” New Approaches for Computer-Assisted Skin Cancer Diagnosis”, The Third International Symposium on Optimization and Systems Biology (OSB’09) Zhangjiajie, China, September 20–22, 2009