Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks

Rehan Raza, Fatima Zulfiqar, Shehroz Tariq, Gull Bano Anwar, Allah Bux Sargano and Zulfiqar Habib
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Rehan Raza: Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan
Fatima Zulfiqar: Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan
Shehroz Tariq: Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan
Gull Bano Anwar: Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan
Allah Bux Sargano: Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan
Zulfiqar Habib: Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan

Mathematics, 2021, vol. 10, issue 1, 1-15

Abstract: Human skin is the most exposed part of the human body that needs constant protection and care from heat, light, dust, and direct exposure to other harmful radiation, such as UV rays. Skin cancer is one of the dangerous diseases found in humans. Melanoma is a form of skin cancer that begins in the cells (melanocytes) that control the pigment in human skin. Early detection and diagnosis of skin cancer, such as melanoma, is necessary to reduce the death rate due to skin cancer. In this paper, the classification of acral lentiginous melanoma, a type of melanoma with benign nevi, is being carried out. The proposed stacked ensemble method for melanoma classification uses different pre-trained models, such as Xception, Inceptionv3, InceptionResNet-V2, DenseNet121, and DenseNet201, by employing the concept of transfer learning and fine-tuning. The selection of pre-trained CNN architectures for transfer learning is based on models having the highest top-1 and top-5 accuracies on ImageNet. A novel stacked ensemble-based framework is presented to improve the generalizability and increase robustness by fusing fine-tuned pre-trained CNN models for acral lentiginous melanoma classification. The performance of the proposed method is evaluated by experimenting on a Figshare benchmark dataset. The impact of applying different augmentation techniques has also been analyzed through extensive experimentations. The results confirm that the proposed method outperforms state-of-the-art techniques and achieves an accuracy of 97.93%.

Keywords: deep learning; transfer learning; skin cancer; acral lentiginous melanoma; melanoma classification; ensemble learning; data augmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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