Abstract: Melanoma is accounted as a rare skin cancer responsible for a huge mortality rate. However, various imaging tests can be used to detect the metastatic spread of disease with a primary diagnosis or on clinical suspicion. Focus on melanoma detection, irrespective of its unusual occurrence, is that it is often misdiagnosed for other skin malignancies leading to medical negligence. Sometimes melanoma is detected only when the metastasis has entered the bloodstream or lymph nodes. So, effective computational strategies for early detection of melanoma are essential. There are four principal types of skin melanoma with two sub types: Superficial spreading, nodular, lentigo, lentigo maligna, Acral lentiginous, and Subungual melanoma. Amelanotic melanoma, one particular type of melanoma, exists in all kinds of skin tones. Classifications of melanoma with its classes are focused on in this research. Misclassification errors, overfitting issues and improve accuracy, the ensemble classifier models, namely Adaboost, random forest, voted ensemble, voted CNN, Boosted SVM, Boosted GMM, have been used in melanoma classification. The results of the ensemble classifier achieve high classification accuracy. However, imbalanced classification is found in all six classes of melanoma. Transfer learning and ensembled transfer learning approaches are implemented to reduce the imbalanced classification issues, and performances are analyzed. Four ML/DL models, six ensembled models, four transfer learning models, and five ensembled transfer learning models are used in this investigation. Implementation of all the 19 classifiers is analyzed using standard performance metrics such as Accuracy, Precision, recall, Mathew’s correlation coefficient, Jaccard Index, F1 measure, and Cohen’s Kappa.

Sarah Ali Alshawi and Ghazwan Fouad Kadhim AI Musawi, “Skin Cancer Image Detection and Classification by CNN based Ensemble Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 14(5), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140575

@article{Alshawi2023,
title = {Skin Cancer Image Detection and Classification by CNN based Ensemble Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140575},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140575},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {5},
author = {Sarah Ali Alshawi and Ghazwan Fouad Kadhim AI Musawi}
}