An Adaptive Segmentation Approach with IWT-AWT Modeling with Hybrid Ensemble Algorithm for Skin Cancer Classification
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 7 |
| Year of Publication : 2023 |
| Authors : Ravi Chandra Bandi, K. Rajendra Prasad, A. Kamalakumari, A. Daisy Rani |
10.14445/23488379/IJEEE-V10I7P107
How to Cite?
Ravi Chandra Bandi, K. Rajendra Prasad, A. Kamalakumari, A. Daisy Rani, "An Adaptive Segmentation Approach with IWT-AWT Modeling with Hybrid Ensemble Algorithm for Skin Cancer Classification," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 7, pp. 76-92, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I7P107
Abstract:
With the common occurrence of the different types of cancers, skin cancer is a frequently observed characteristic problem rooted in significant body parts. Many problems related to identifying the different types of cancer are to be analyzed that are not visible, or the type is quite complicated to identify the root of the problem and its occurrence. Deep learning methods and other machine learning models have been developed to introduce the overall design perspective indicating the practical approach to identification and diagnostics. Due to substantial changes in human characteristic traits/behaviour, the need of importance on lesion detection is enactive to implicate the correct problem. One such feature with region identification and transformative approach is improvised to diagnose the area and regions of cancers as effectively as possible. Our proposed approaches with IWT-AWT (Improved Watershed Transform - Active Wavelet Transform) have implicated the region structural changes via probabilistic technique to identify the correct and nearest region of the growth, indicating the overall feature traits to indicate the identification of the cancerous region. This approach (IWT-AWT) for which skin cancer on this perspective was to analyze the problem of early detection via segmentation. The overall expected conditionality approaches on the IWT-AWT algorithms have been inculcated to realize the 450 training and testing images with melanoma, Nevus, and other lesion identifications in MATLAB; we have observed the different classification accuracies, precision, and other performance metrics (Sensitivity and Specificity) are considered for the machine learning algorithms having the overall accuracies for most of the design algorithm based on IWT-AWT is 99%. On a different comparative scale, we have introduced CNN, which provides classification accuracy at 90%.
Keywords:
AWT (Active Wavelet Transform), CNN (Convolutional Neural Networks), EC (Expected Conditionality), IWT (Improved Watershed Transform), KNN (K – Nearest Neighbours), SVM (Support Vector Machine).
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