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Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction

  • Image & Signal Processing
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Abstract

Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed malignant or benign – depending upon the severity of the infection and the current stage. The conventional methods require a detailed physical inspection by an expert dermatologist, which is time-consuming and imprecise. Therefore, several computer vision methods are introduced lately, which are cost-effective and somewhat accurate. In this work, we propose a new automated approach for skin lesion detection and recognition using a deep convolutional neural network (DCNN). The proposed cascaded design incorporates three fundamental steps including; a) contrast enhancement through fast local Laplacian filtering (FlLpF) along HSV color transformation; b) lesion boundary extraction using color CNN approach by following XOR operation; c) in-depth features extraction by applying transfer learning using Inception V3 model prior to feature fusion using hamming distance (HD) approach. An entropy controlled feature selection method is also introduced for the selection of the most discriminant features. The proposed method is tested on PH2 and ISIC 2017 datasets, whereas the recognition phase is validated on PH2, ISBI 2016, and ISBI 2017 datasets. From the results, it is concluded that the proposed method outperforms several existing methods and attained accuracy 98.4% on PH2 dataset, 95.1% on ISBI dataset and 94.8% on ISBI 2017 dataset.

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References

  1. Rogers, H. W., Weinstock, M. A., Feldman, S. R., and Coldiron, B. M., Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US population, 2012. JAMA Dermatology 151(10):1081–1086, 2015.

    Article  PubMed  Google Scholar 

  2. Jerant, A. F., Johnson, J. T., Demastes Sheridan, C., and Caffrey, T. J., Early detection and treatment of skin cancer. American family physician 62(2), 2000.

  3. Balch, C. M., Gershenwald, J. E., Soong, S.-J., Thompson, J. F., Atkins, M. B., Byrd, D. R. et al., Final version of 2009 AJCC melanoma staging and classification. Journal of clinical oncology 27(36):6199, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H., and Saba, T., CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Computers and Electronics in Agriculture 155:220–236, 2018.

    Article  Google Scholar 

  5. Nasir, M., Khan, M. A., Sharif, M., Lali, I. U., Saba, T., Iqbal, T., An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach, Microscopy research and technique 81(6):528–543, 2018. https://doi.org/10.1002/jemt.23009

    Article  PubMed  Google Scholar 

  6. Binder, M., Schwarz, M., Winkler, A., Steiner, A., Kaider, A., Wolff, K., and Pehamberger, H., Epiluminescence microscopy: a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Archives of Dermatology 131(3):286–291, 1995.

    Article  CAS  PubMed  Google Scholar 

  7. Abbas, Q., Garcia, I., and Rashid, M., Automatic skin tumor border detection for digital dermoscopy using a new digital image analysis scheme. British Journal of Biomedical Science 67(4):177–183, 2010.

    Article  CAS  PubMed  Google Scholar 

  8. Yu, L., Chen, H., Dou, Q., Qin, J., and Heng, P.-A., Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Transactions on Medical Imaging 36(4):994–1004, 2017.

    Article  PubMed  Google Scholar 

  9. Korotkov, K., and Garcia, R., Computerized analysis of pigmented skin lesions: a review. Artificial Intelligence in Medicine 56(2):69–90, 2012.

    Article  PubMed  Google Scholar 

  10. Khan, M. A., Akram, T., Sharif, M., Saba, T., Javed, K., Lali, I. U. et al., Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion. Microsc Res Tech, 2019a. https://doi.org/10.1002/jemt.23220.

    Article  CAS  PubMed  Google Scholar 

  11. Khan, S. A., Nazir, M., Khan, M. A., Saba, T., Javed, K., Rehman, A., Akram, T., and Awais, M., Lungs nodule detection framework from computed tomography images using support vector machine. Microsc Res Tech., 2019b. https://doi.org/10.1002/jemt.23275.

    Article  PubMed  Google Scholar 

  12. Khan, M. A., Lali, I. U., Rehman, A., Ishaq, M., Sharif, M., Saba, T., Zahoor, S., and Akram, T., Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection. Microscopy Research and Technique, 2019c. https://doi.org/10.1002/jemt.23238.

    Article  PubMed  Google Scholar 

  13. Yousaf, K., Mehmood, Z., Saba, T., Rehman, A., Munshi, A. M., Alharbey, R., and Rashid, M., Mobile-health applications for the efficient delivery of health care facility to people with dementia (PwD) and support to their carers: A survey. BioMed Research International 2019:1–26, 2019.

    Article  Google Scholar 

  14. Suzuki, K., Overview of deep learning in medical imaging. Radiological Physics and Technology 10(3):257–273, 2017.

    Article  PubMed  Google Scholar 

  15. Shen, D., Wu, G., and Suk, H.-I., Deep learning in medical image analysis. Annual Review of Biomedical Engineering 19:221–248, 2017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P. et al., Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface 15(141):20170387, 2018.

    Article  PubMed Central  Google Scholar 

  17. Norouzi, A., Rahim, M. S. M., Altameem, A., Saba, T., Rada, A. E., Rehman, A., and Uddin, M., Medical image segmentation methods, algorithms, and applications. IETE Technical Review 31(3):199–213, 2014. https://doi.org/10.1080/02564602.2014.906861.

    Article  Google Scholar 

  18. Lee, T., Ng, V., Gallagher, R., Coldman, A., and McLean, D., Dullrazor®: A software approach to hair removal from images. Computers in Biology and Medicine 27(6):533–543, 1997.

    Article  CAS  PubMed  Google Scholar 

  19. Kiani, K., and Sharafat, A. R., E-shaver: An improved DullRazor® for digitally removing dark and light-colored hairs in dermoscopic images. Computers in Biology and Medicine 41(3):139–145, 2011.

    Article  PubMed  Google Scholar 

  20. Liu, Z., and Zerubia, J., Skin image illumination modeling and chromophore identification for melanoma diagnosis. Physics in Medicine & Biology 60(9):3415, 2015.

    Article  Google Scholar 

  21. Emre Celebi, M., Kingravi, H. A., Iyatomi, H., Alp Aslandogan, Y., Stoecker, W. V., Moss, R. H. et al., Border detection in dermoscopy images using statistical region merging. Skin Research and Technology 14(3):347–353, 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Gómez, D. D., Butakoff, C., Ersboll, B. K., and Stoecker, W., Independent histogram pursuit for segmentation of skin lesions. IEEE Transactions on Biomedical Engineering 55(1):157–161, 2008.

    Article  PubMed  Google Scholar 

  23. Silveira, M., Nascimento, J. C., Marques, J. S., Marçal, A. R., Mendonça, T., Yamauchi, S. et al., Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE Journal of Selected Topics in Signal Processing 3(1):35–45, 2009.

    Article  Google Scholar 

  24. Yao, Q., Guan, Z., Zhou, Y., Tang, J., Hu, Y., & Yang, B. (2009). Application of support vector machine for detecting rice diseases using shape and color texture features. Paper presented at the Engineering Computation, 2009. ICEC'09. International Conference on.

  25. Sadeghi, M., Lee, T. K., McLean, D. I., Lui, H., and Atkins, M. S., Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans. Med. Imaging 32(5):849–861, 2013.

    Article  PubMed  Google Scholar 

  26. Satheesha, T., Satyanarayana, D., Prasad, M. G., and Dhruve, K. D., Melanoma is Skin Deep: A 3D reconstruction technique for computerized dermoscopic skin lesion classification. IEEE Journal of Translational Engineering in Health and Medicine 5:1–17, 2017.

    Article  Google Scholar 

  27. Celebi, M. E., and Zornberg, A., Automated quantification of clinically significant colors in dermoscopy images and its application to skin lesion classification. IEEE systems journal 8(3):980–984, 2014.

    Article  Google Scholar 

  28. Tschandl, P., Sinz, C., and Kittler, H., Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation. Computers in biology and medicine 104:111–116, 2019.

    Article  PubMed  Google Scholar 

  29. Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Pitiot, A., and Wang, C., Fusing fine-tuned deep features for skin lesion classification. Computerized Medical Imaging and Graphics 71:19–29, 2019.

    Article  PubMed  Google Scholar 

  30. Rebouças Filho, P. P., Peixoto, S. A., da Nóbrega, R. V. M., Hemanth, D. J., Medeiros, A. G., Sangaiah, A. K., and de Albuquerque, V. H. C., Automatic histologically-closer classification of skin lesions. Computerized Medical Imaging and Graphics., 2018.

  31. Oliveira, R. B., Papa, J. P., Pereira, A. S., and Tavares, J. M. R., Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Computing and Applications 29(3):613–636, 2018.

    Article  Google Scholar 

  32. Ray, S. (2018). Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest. arXiv preprint arXiv:1807.05711.

  33. Eaton-Rosen, Z., Bragman, F., Ourselin, S., & Cardoso, M. J. (2018). Improving Data Augmentation for Medical Image Segmentation.

  34. Paris, S., Hasinoff, S. W., and Kautz, J., Local Laplacian filters: Edge-aware image processing with a Laplacian pyramid. ACM Trans. Graph. 30(4):61–68, 2011 12.

    Article  Google Scholar 

  35. Kaur, R., Stoecker, W. V. D., Mishra, N. K., & Kasmi, R. (2018). Thresholding methods for lesion segmentation in dermoscopy images: Google Patents.

  36. Guo, Y., Ashour, A. S., and Smarandache, F., A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images. Symmetry 10(4):119, 2018.

    Article  Google Scholar 

  37. Xu, H., & Hwang, T. H. (2018). Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks. arXiv preprint arXiv:1807.06466.

  38. Jamal, A., Hazim Alkawaz, M., Rehman, A., and Saba, T., (2017) Retinal imaging analysis based on vessel detection. Microsc Res Tech. 80(17):799–811, 2017. https://doi.org/10.1002/jemt.

    Article  PubMed  Google Scholar 

  39. Rahim, M. S. M., Rehman, A., Kurniawan, F., and Saba, T., Ear biometrics for human classification based on region features mining. Biomedical Research, vol. 28(10):4660–4664, 2017.

    Google Scholar 

  40. Duan, Q., Akram, T., Duan, P., & Wang, X., Visual saliency detection using information contents weighting. Optik 127(19):7418–7430, 2016.

  41. Mughal, B., Muhammad, N., Sharif, M., Rehman, A., and Saba, T., Removal of pectoral muscle based on topographic map and shape-shifting silhouette. BMC Cancer, 2018. https://doi.org/10.1186/s12885-018-4638-5.

  42. Mughal, B., Muhammad, N., Sharif, M., Saba, T., and Rehman, A., Extraction of breast border and removal of pectoral muscle in wavelet, domain. Biomedical Research 28(11):5041–5043, 2017a.

    Google Scholar 

  43. Mughal, B., Sharif, M., Muhammad, N., and Saba, T., A novel classification scheme to decline the mortality rate among women due to breast tumor. Microscopy Research and Technique, 2017b. https://doi.org/10.1002/jemt.22961.

    Article  PubMed  Google Scholar 

  44. Saba, T., Khan, S. U., Islam, N., Abbas, N., Rehman, A. et al., Cloud-based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images. Microscopy Research and Technique, 2019. https://doi.org/10.1002/jemt.23222.

    Article  PubMed  Google Scholar 

  45. Saba, T., Al-Zahrani, S., and Rehman, A., Expert system for offline clinical guidelines and treatment. Life Sci Journal 9(4):2639–2658, 2012.

    Google Scholar 

  46. Iqbal, S., Khan, M. U. G., Saba, T., Mehmood, Z., Javaid, N., Rehman, A., and Abbasi, R., Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation. Microscopy Research and Technique, 2019. https://doi.org/10.1002/jemt.23281.

    Article  PubMed  Google Scholar 

  47. Saba, T., Rehman, A., Mehmood, Z., Kolivand, H., and Sharif, M., Image Enhancement and Segmentation Techniques for Detection of Knee Joint Diseases: A Survey. Current Medical Imaging Reviews 14(5), 2018b. https://doi.org/10.2174/1573405613666170912164546.

    Article  Google Scholar 

  48. Saba, T., Bokhari, S. T. F., Sharif, M., Yasmin, M., and Raza, M., Fundus image classification methods for the detection of glaucoma: A review. Microsc Res Tech., 2018a. https://doi.org/10.1002/jemt.23094.

    Article  PubMed  Google Scholar 

  49. Sadad, T., Munir, A., Saba, T., and Hussain, A., Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature. Journal of Computational Science 29:34–45, 2018.

    Article  Google Scholar 

  50. Ullah, H., Saba, T., Islam, N., Abbas, N., Rehman, A., Mehmood, Z., Anjum, A., An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection, Microscopy research and technique, 2019. https://doi.org/10.1002/jemt.23178.

    Article  PubMed  Google Scholar 

  51. Iqbal, S., Ghani, M. U., Saba, T., and Rehman, A., Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microsc Res Tech. 81(4):419–427, 2018. https://doi.org/10.1002/jemt.22994.

    Article  PubMed  Google Scholar 

  52. Iqbal, S., Khan, M. U. G., Saba, T., and Rehman, A., Computer assisted brain tumor type discrimination using magnetic resonance imaging features. Biomedical Engineering Letters 8(1):5–28, 2017. https://doi.org/10.1007/s13534-017-0050-3.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Tahir, B. Iqbal, S., Khan, M.U.G., Saba, T., Mehmood, Z., Anjum, A., Mahmood, T. (2019) Feature enhancement framework for brain tumor segmentation and classification, https://doi.org/10.1002/jemt.23224.

    Article  PubMed  Google Scholar 

  54. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.

  55. Sharif, M., Khan, M. A., Akram, T., Javed, M. Y., Saba, T., and Rehman, A., A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection. EURASIP Journal on Image and Video Processing 2017(1):89, 2017.

    Article  Google Scholar 

  56. Bi, L., Kim, J., Ahn, E., Kumar, A., Feng, D., and Fulham, M., Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern recognition 85:78–89, 2019.

    Article  Google Scholar 

  57. Wu, J. T., Dernoncourt, F., Gehrmann, S., Tyler, P. D., Moseley, E. T., Carlson, E. T. et al., Behind the scenes: A medical natural language processing project. International Journal of Medical Informatics 112:68–73, 2018.

    Article  PubMed  Google Scholar 

  58. Codella, N. C., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., … Kittler, H. (2018). 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). Paper presented at the Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on.

  59. Navarro, F., Escudero-Vinolo, M., and Bescos, J., Accurate segmentation and registration of skin lesion images to evaluate lesion change. IEEE Journal of Biomedical and Health Informatics, 2018.

  60. Li, Y., and Shen, L., Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2):556, 2018.

    Article  Google Scholar 

  61. Soudani, A., and Barhoumi, W., An image-based segmentation recommender using crowdsourcing and transfer learning for skin lesion extraction. Expert Systems with Applications 118:400–410, 2019.

    Article  Google Scholar 

  62. Gutiérrez-Arriola, J. M., Gómez-Álvarez, M., Osma-Ruiz, V., Sáenz-Lechón, N., & Fraile, R. (2017). Skin lesion segmentation based on preprocessing, thresholding and neural networks. arXiv preprint arXiv:1703.04845.

  63. Bi, L., Kim, J., Ahn, E., Kumar, A., Fulham, M., and Feng, D., Dermoscopic image segmentation via multi-stage fully convolutional networks. IEEE Trans. Biomed. Eng 64(9):2065–2074, 2017a.

    Article  PubMed  Google Scholar 

  64. Waheed, Z., Waheed, A., Zafar, M., & Riaz, F. (2017). An efficient machine learning approach for the detection of melanoma using dermoscopic images. Paper presented at the Communication, Computing and Digital Systems (C-CODE), International Conference on.

  65. Akram, T., Khan, M. A., Sharif, M., & Yasmin, M., Skin lesion segmentation and recognition using multichannel saliency estimation and MSVM on selected serially fused features. Journal of Ambient Intelligence and Humanized Computing, 1–-20, 2018.

  66. Sultana, N. N., Mandal, B., and Puhan, N., Deep residual network with regularised fisher framework for detection of melanoma. IET Computer Vision 12(8):1096–1104, 2018.

    Article  Google Scholar 

  67. Sarker, M., Kamal, M., Rashwan, H. A., Banu, S. F., Saleh, A., Singh, V. K., … Radeva, P. (2018). SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. arXiv preprint arXiv:1805.10241.

  68. Bi, L., Kim, J., Ahn, E., & Feng, D. (2017b). Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. arXiv preprint arXiv:1703.04197.

  69. Afza, F., Khan, M. A., Sharif, M., and Rehman, A., Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection. Microscopy Research and Technique., 2019. https://doi.org/10.1002/jemt.23301.

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Acknowledgements

This work was supported by the research Project [Skin Cancer Melanoma Detection from Dermoscopic Images Using Machine Learning Techniques]; Prince Sultan University; Saudi Arabia [SSP -18-5-04]Additionally, in part supported by Artificial Intelligence and Data Analytics (AIDA) Lab Prince Sultan University Riyadh Saudi Arabia. Authors are thankful for the support.

This work was supported by the Research Project (SSP -18-5-04). Additionally, in part supported by Artificial Intelligence and Data Analytics (AIDA) Lab Prince Sultan University Riyadh Saudi Arabia. Authors are thankful for the support.

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Authors and Affiliations

  1. College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Tanzila Saba & Souad Larabi Marie-Sainte

  2. Department of Computer Science and Engineering, HITEC Universit, Museum Road, Taxila, Pakistan

    Muhammad Attique Khan

  3. College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia

    Amjad Rehman

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  1. Tanzila Saba
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  4. Souad Larabi Marie-Sainte
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Correspondence to Amjad Rehman.

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Highlights

• This research presents a novel approach for microscopic skin lesion boundary extraction and lesion recognition through Deep Neural Network.

• Distinction features are selected through clustering controlled entropy approach and classified through MLP.

This article is part of the Topical Collection on Image & Signal Processing

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Saba, T., Khan, M.A., Rehman, A. et al. Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction. J Med Syst 43, 289 (2019). https://doi.org/10.1007/s10916-019-1413-3

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