Skin Lesion Detection and Classification Using Convolutional Neural Network for Deep Feature Extraction and Support Vector Machine

Oscar P. Yanchatuña, Jackeline P. Pereira, Kevin O. Pila, Paola A. Vásquez, Keiko S. Veintimilla, Gandhi F. Villalba-Meneses, Omar Alvarado-Cando, Diego Almeida-Galárraga


Pigmented skin lesion identification is essential for detecting harmful pathologies related to this large organ, especially cancer. An analysis of the different methods and projects developed to diagnose these illnesses throughout the years showed that they had become very useful tools to identify melanoma, dermatofibroma, and basal cell carcinoma, among other types of cancer, are seen through the use of new computer-aided technologies. The most common diagnosis is based on dermoscopy and the dermatologist expertise that can improve accuracy with image detection techniques and classification by computer. Therefore, this study aims to develop software models able to detect and classify skin cancer. The following work is based on the use of dermoscopy images obtained from the HAM10000 dataset, a database with 10000 images previously tested and validated for research use. The main process is divided into three relevant parts: image segmentation, feature extraction (FE) using ten different pre-trained Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM) to establish a classification model. According to the results, the models of classification performed very well using the image segmentation step, showing average accuracies between 80.67% (Xception) and 90% (Alexnet). In contrast to the process without using image segmentation, where no method reached 60%. AlexNet plus SVM model showed the minor running time and presented the higher accuracy rate (90.34%) for the correct identification and classification of the seven categories of cutaneous lesions taken into account.


Skin lesions; melanoma; image segmentation; image preprocessing; convolutional neural network; HAM10000.

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V. Corral et al., “Estudio Descriptivo: Características del Cáncer de Piel no Melanoma en Pacientes de Consulta Externa de Dermatología del Hospital Vicente Corral Moscoso. Cuenca – Ecuador, 2013-2017.,†Rev. MÉDICA HJCA, vol. 11, pp. 2013–2017, 2019.

F. Liu-Smith, J. Jia, and Y. Zheng, “UV-induced molecular signaling differences in melanoma and non-melanoma skin cancer,†Adv. Exp. Med. Biol., vol. 996, pp. 27–40, 2017, doi: 10.1007/978-3-319-56017-5_3.

D. Schadendorf et al., “Melanoma,†Lancet, vol. 392, no. 10151, pp. 971–984, 2018, doi: 10.1016/S0140-6736(18)31559-9.

N. Razmjooy et al., “Computer-aided Diagnosis of Skin Cancer: A Review,†Curr. Med. Imaging Former. Curr. Med. Imaging Rev., vol. 16, no. 7, pp. 781–793, Sep. 2020, doi: 10.2174/1573405616666200129095242.

L. Ferrante di Ruffano et al., “Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults,†Cochrane Database Syst. Rev., vol. 53, no. 9, pp. 1689–1699, Dec. 2018, doi: 10.1002/14651858.CD013186.

D. Caratelli, A. Massaro, R. Cingolani, and A. G. Yarovoy, “Accurate Time-Domain Modeling of Reconfigurable Antenna Sensors for Non-Invasive Melanoma Skin Cancer Detection,†IEEE Sens. J., vol. 12, no. 3, pp. 635–643, Mar. 2012, doi: 10.1109/JSEN.2011.2117417.

V. Narayanamurthy et al., “Skin cancer detection using non-invasive techniques,†RSC Adv., vol. 8, no. 49, pp. 28095–28130, 2018, doi: 10.1039/C8RA04164D.

U. B. Ansari, “Skin Cancer Detection Using Image Processing Tanuja Sarode 2,†Int. Res. J. Eng. Technol., vol. 4, no. 4, pp. 2395–56, 2017, [Online]. Available:

P. M. M. Pereira et al., “Dermoscopic skin lesion image segmentation based on Local Binary Pattern Clustering: Comparative study,†Biomed. Signal Process. Control, vol. 59, p. 101924, 2020, doi: 10.1016/j.bspc.2020.101924.

K. Matsunaga, A. Hamada, A. Minagawa, and H. Koga, “Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble,†arXiv, pp. 2–5, 2017.

P. Tschandl, C. Rosendahl, and H. Kittler, “Data descriptor: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,†Sci. Data, vol. 5, pp. 1–9, 2018, doi: 10.1038/sdata.2018.161.

F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey,†Neurocomputing, vol. 292, pp. 1–27, 2018, doi: 10.1016/j.neucom.2018.01.091.

A. Adegun and S. Viriri, Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art, no. 0123456789. Springer Netherlands, 2020.

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,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9352, pp. 118–126, 2015, doi: 10.1007/978-3-319-24888-2_15.

M. van Eijnatten, R. van Dijk, J. Dobbe, G. Streekstra, J. Koivisto, and J. Wolff, “CT image segmentation methods for bone used in medical additive manufacturing,†Med. Eng. Phys., vol. 51, pp. 6–16, 2018, doi: 10.1016/j.medengphy.2017.10.008.

E. Pérez, O. Reyes, and S. Ventura, “Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study,†Med. Image Anal., vol. 67, p. 101858, Jan. 2021, doi: 10.1016/

M. A. Khan et al., “An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification,†BMC Cancer, vol. 18, no. 1, pp. 1–20, 2018, doi: 10.1186/s12885-018-4465-8.

Y. Li and L. Shen, “Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network,†Sensors, vol. 18, no. 2, p. 556, Feb. 2018, doi: 10.3390/s18020556.

P. Smith, D. B. Reid, C. Environment, L. Palo, P. Alto, and P. L. Smith, “NOBUYUKI OTSU. - 1979 - A Tlreshold Selection Method from Gray-Level Histograms,†IEEE Trans. Syst. Man Cybern., vol. 20, no. 1, pp. 62–66, 1979.

W. Rawat and Z. Wang, “Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review,†Neural Comput., vol. 29, no. 9, pp. 2352–2449, Sep. 2017, doi: 10.1162/neco_a_00990.

T. J. Brinker et al., “Skin cancer classification using convolutional neural networks: Systematic review,†J. Med. Internet Res., vol. 20, no. 10, pp. 1–8, 2018, doi: 10.2196/11936.

T. J. Brinker, A. Hekler, A. H. Enk, and C. von Kalle, “Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions,†PLoS One, vol. 14, no. 6, pp. 1–8, 2019, doi: 10.1371/journal.pone.0218713.

T. Shanthi, R. S. Sabeenian, and R. Anand, “Automatic diagnosis of skin diseases using convolution neural network,†Microprocess. Microsyst., vol. 76, p. 103074, 2020, doi: 10.1016/j.micpro.2020.103074.

G. S. Saragih, Z. Rustam, D. Aldila, R. Hidayat, R. E. Yunus, and J. Pandelaki, “Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 5, p. 2177, 2020, doi: 10.18517/ijaseit.10.5.13000.

N. C. F. Codella et al., “Deep learning ensembles for melanoma recognition in dermoscopy images,†IBM J. Res. Dev., vol. 61, no. 4–5, pp. 1–15, 2017.

J. Kawahara, A. BenTaieb, and G. Hamarneh, “Deep features to classify skin lesions,†in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Apr. 2016, pp. 1397–1400, doi: 10.1109/ISBI.2016.7493528.

B. Lei et al., “Skin lesion segmentation via generative adversarial networks with dual discriminators,†Med. Image Anal., vol. 64, p. 101716, 2020, doi: 10.1016/

E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640–651, Apr. 2017, doi: 10.1109/TPAMI.2016.2572683.

C. Xu and J. L. Prince, “Gradient Vector Flow,†in Computer Vision, Cham: Springer International Publishing, 2020, pp. 1–8.

J. Dogra, N. Prashar, S. Jain, and M. Sood, “Improved methods for analyzing MRI brain images,†vol. 8, no. 1, pp. 1–11, 2018.

D. Marmanis, K. Schindler, J. D. Wegner, S. Galliani, M. Datcu, and U. Stilla, “Classification with an edge: Improving semantic image segmentation with boundary detection,†ISPRS J. Photogramm. Remote Sens., vol. 135, pp. 158–172, 2018, doi: 10.1016/j.isprsjprs.2017.11.009.

J. L. Garcia-Arroyo and B. Garcia-Zapirain, “Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding,†Comput. Methods Programs Biomed., vol. 168, pp. 11–19, Jan. 2019, doi: 10.1016/j.cmpb.2018.11.001.

Y. Feng, H. Zhao, X. Li, X. Zhang, and H. Li, “A multi-scale 3D Otsu thresholding algorithm for medical image segmentation,†Digit. Signal Process. A Rev. J., vol. 60, pp. 186–199, 2017, doi: 10.1016/j.dsp.2016.08.003.

S. Krishnan and Y. Athavale, “Trends in biomedical signal feature extraction,†Biomed. Signal Process. Control, vol. 43, pp. 41–63, 2018, doi: 10.1016/j.bspc.2018.02.008.

O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,†Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y.

A. Dhillon and G. K. Verma, “Convolutional neural network: a review of models, methodologies and applications to object detection,†Prog. Artif. Intell., vol. 9, no. 2, pp. 85–112, 2020, doi: 10.1007/s13748-019-00203-0.

P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Deep feature based rice leaf disease identification using support vector machine,†Comput. Electron. Agric., vol. 175, no. May, p. 105527, 2020, doi: 10.1016/j.compag.2020.105527.

O. Okwuashi and C. E. Ndehedehe, “Deep support vector machine for hyperspectral image classification,†Pattern Recognit., vol. 103, p. 107298, 2020, doi: 10.1016/j.patcog.2020.107298.

X. Peng, L. Li, and F. Y. Wang, “Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling,†IEEE Trans. Neural Networks Learn. Syst., vol. 31, no. 11, pp. 4649–4659, 2020, doi: 10.1109/TNNLS.2019.2957003.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,†Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.

M. A. Khan, M. Y. Javed, M. Sharif, T. Saba, and A. Rehman, “Multi-Model Deep Neural Network based Features Extraction and Optimal Selection Approach for Skin Lesion Classification,†in 2019 International Conference on Computer and Information Sciences (ICCIS), Apr. 2019, pp. 1–7, doi: 10.1109/ICCISci.2019.8716400.



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