A novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Naresh Kumar Sripada; Ismail. B Mohammed

doi:10.53730/ijhs.v6nS1.8611

Authors

Naresh Kumar Sripada
naresh22.in@gmail.com
Research Scholar, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation Vaddeswaram, A.P, India
Mohammed Ismail. B Associate Professor, Department of Information Technology, Kannur University, Mangattuparamba, Kannur, Kerala, India

Keywords:

Melanoma, Skin malignancy, Deep Neural Network, Convolutional Neural Network (CNN)

Abstract

Melanoma is a malignant skin disease that kills roughly one million people per year, as per WHO records. The proposed work's major goal is to create and develop an enhanced deep neural network model that can evaluate, recognize, and predict cutaneous skin disease in its early stages with more accuracy, lowering the chance of death. The dataset, which was gathered from dermatologists and the public domain, contains roughly 3606 images of various types of skin lesions; some of the images were distorted. Gaussian and bilateral filters were used to decrease the noise in the images. After cleaning the data, the proposed six Convolutional layered Deep-CNN networks deployed to identify and predict skin cancer. The proposed CNN network was able to recognize skin diseases such Malignant Melanoma, Squamous cellcarcinoma, and Basal cellcarcinoma with an optimal error rate and good accuracy of 98.07 percent.

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References

Li-sheng Wei, Quan Gan, Tao Ji, "Skin Disease Recognition Method Based on Image Color and Texture Features", Computational and Mathematical Methods in Medicine, vol. 2018, https://doi.org/10.1155/2018/8145713.

Skin Cancer Facts & Statistics Melanoma. https://www.skincancer.org/skin-cancer-information/skin-cancer-facts/#melanoma. Accessed 31 Oct 2018

Puttagunta, M., Ravi, S. Medical image analysis based on deep learning approach. Multimed Tools Appl 80, 24365–24398 (2021). https://doi.org/10.1007/s11042-021-10707-4

Md Shahin Ali, Md Sipon Miah, Jahurul Haque, Md Mahbubur Rahman, Md Khairul Islam, An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models, Machine Learning with Applications, Volume 5, 2021, 100036,https://doi.org/10.1016/j.mlwa.2021.100036.

Nawal Soliman ALKolifi ALEnezi, A Method Of Skin Disease Detection Using Image Processing And Machine Learning, Procedia Computer Science, Volume 163, 2019,Pages 85-92, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.12.090

Soumyya Kanti Datta, Mohammad Abuzar Shaikh, Sargur N Srihari, Mingchen Gao, Soft-Attention Improves Skin Cancer Classification Performance, doi: https://doi.org/10.1101/2021.05.12.21257114

Nasiri, S., Helsper, J., Jung, M. et al. DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images. BMC Bioinformatics 21, 84 (2020). https://doi.org/10.1186/s12859-020-3351-y

Hosny KM, Kassem MA, Foaud MM (2019) Classification of skin lesions using transfer learning and augmentation with Alex-net. PLoS ONE 14(5): e0217293. https://doi.org/10.1371/journal.pone.0217293

Gessert, N., Sentker, T., Madesta, F., Schmitz, R., Kniep, H., Baltruschat, I., et al. (2020). Skin Lesion Classification Using Cnns with Patch-Based Attention and Diagnosis-Guided Loss Weighting. IEEE Trans. Biomed. Eng. 67, 495–503. doi:10.1109/TBME.2019.2915839

Mustafa S, Kimura A. A SVM-based diagnosis of melanoma using only useful image features. International Workshop on Advanced Image Technology. IEEE: 2018. https://doi.org/10.1109/iwait.2018.8369646.

Capdehourat, G., Corez, A., Bazzano, A., Alonso, R., and Musé, P. (2011). Toward a Combined Tool to Assist Dermatologists in Melanoma Detection from Dermoscopic Images of Pigmented Skin Lesions. Pattern Recognition Lett. 32, 2187–2196. doi:10.1016/j.patrec.2011.06.015

Zakeri A, Soukhtesaraie S. Automatic Diagnosis of Melanoma Using Log-Linearized Gaussian Mixture Network. In: 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME). IEEE: 2017.

Ho, T.T., Kim, T., Kim, W.J. et al. A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects. Sci Rep 11, 34 (2021). https://doi.org/10.1038/s41598-020-79336-5

Du, R. et al. Identification of COPD from multi-view snapshots of 3D lung airway tree via deep CNN. IEEE Access 8, 38907–38919 (2020)

Bodduluri, S., Newell, J. D. Jr., Hoffman, E. A. & Reinhardt, J. M. Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework. Acad. Radiol. 20, 527–536 (2013).

Sripada, Naresh Kumar, et al. "Classification And Clustering Of Gene Expression In The Form Of Microarray And Prediction Of Cancersusceptibilit, Cancerrecurrence And Cancersurvival." Journal Of Mechanics Of Continua And Mathematical Sciences 15 (2020)

Kumar, S.N., et al. A Novel Approach for Detection of Counterfeit Indian Currency Notes Using Deep Convolutional Neural Network. In IOP Conference Series: Materials Science and Engineering 981(2) pp 022018. (2020)

Codella N, et al. "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)". (2017)

Ismail, M., Vardhan, V. H., Mounika, V. A., & Padmini, K. S. An Effective Heart Disease Prediction Method using Artificial Neural Network. International Journal of Innovative Technology and Exploring Engineering’ pp.1529-1532

Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5, 180161 (2018).

Rinartha, K., & Suryasa, W. (2017). Comparative study for better result on query suggestion of article searching with MySQL pattern matching and Jaccard similarity. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE.

Sirikonda, Shwetha, et al. "Forecast the death and recovery rate of COVID 2019 using ARIMA and PROPHET models." AIP Conference Proceedings. Vol. 2418. No. 1. AIP Publishing LLC, 2022.

Borra, Surya Prasada Rao, et al. "Google’s new AI technology detects cardiac issues using retinal scan." Applied Nanoscience (2022): 1-8.

Alam, Mansoor, et al. "A machine learning classification technique for predicting prostate cancer." 2020 IEEE International Conference on Electro Information Technology (EIT). IEEE, 2020.

Kumar, S. Naresh, and B. Mohammed Ismail. "Systematic investigation on Multi-Class skin cancer categorization using machine learning approach." Materials Today: Proceedings (2020).

Nyemeesha, V., and B. Mohammed Ismail. "Implementation of noise and hair removals from dermoscopy images using hybrid Gaussian filter." Network Modeling Analysis in Health Informatics and Bioinformatics 10.1 (2021): 1-10.

Kumar Agarwal, A., et al. "Machine Learning Technique for the Assembly-Based Image Classification System." J Nucl Ene Sci Power Generat Techno 10.9 (2021).