Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms

Sudeep D. Thepade, Gaurav Ramnani

Source Title: International Journal of Healthcare Information Systems and Informatics (IJHISI)16(4)

ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799859819|DOI: 10.4018/IJHISI.20211001.oa24

MLA

Thepade, Sudeep D., and Gaurav Ramnani. "Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms." IJHISI vol.16, no.4 2021: pp.1-15. http://doi.org/10.4018/IJHISI.20211001.oa24

APA

Thepade, S. D. & Ramnani, G. (2021). Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(4), 1-15. http://doi.org/10.4018/IJHISI.20211001.oa24

Chicago

Thepade, Sudeep D., and Gaurav Ramnani. "Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms," International Journal of Healthcare Information Systems and Informatics (IJHISI) 16, no.4: 1-15. http://doi.org/10.4018/IJHISI.20211001.oa24

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Abstract

Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.