Abstract
Skin cancer is a widespread disease associated with eight diagnostic classes. The diagnosis of multiple types of skin cancer is a challenging task for dermatologists due to the similarity of skin cancer classes in phenotype. The average accuracy of multiclass skin cancer diagnosis is 62% to 80%. Therefore, the classification of skin cancer using machine learning can be beneficial in the diagnosis and treatment of the patients. Several researchers developed skin cancer classification models for binary class but could not extend the research to multiclass classification with better performance ratios. We have developed deep learning-based ensemble classification models for multiclass skin cancer classification. Experimental results proved that the individual deep learners perform better for skin cancer classification, but still the development of ensemble is a meaningful approach since it enhances the classification accuracy. Results show that the accuracy of individual learners of ResNet, InceptionV3, DenseNet, InceptionResNetV2, and VGG-19 are 72%, 91%, 91.4%, 91.7% and 91.8%, respectively. The accuracy of proposed majority voting and weighted majority voting ensemble models are 98% and 98.6%, respectively. The accuracy of proposed ensemble models is higher than the individual deep learners and the dermatologists’ diagnosis accuracy. The proposed ensemble models are compared with the recently developed skin cancer classification approaches. The results show that the proposed ensemble models outperform recently developed multiclass skin cancer classification models.
Highlights
Cancer can cause death if not diagnosed and treated in a timely fashion and can start almost anywhere in the human body
Better-performing heterogeneous ensemble models were developed for multiclass skin cancer classification using majority voting and weighted majority voting
It is observed from the results that the proposed ensemble models have outperformed both dermatologists and the recently developed deep learning methods for multiclass skin cancer classification
Summary
Cancer can cause death if not diagnosed and treated in a timely fashion and can start almost anywhere in the human body. If skin cancer is diagnosed early, it can usually be treated. There are eight categories of skin cancers: melanoma (MEL), melanocytic nevi (NV), basal cell carcinoma (BCC), benign keratosis lesions (BKL), actinic keratosis (AK), dermatofibroma (DF), squamous cell carcinoma (SCC), and vascularl Lesions (VASC) [1]. MEL is the most dangerous type of cancer, as it spreads to other organs very rapidly It develops in body cells called melanocytes. NV are pigmented moles and vary in different colors of skin tones. It mostly develops during childhood and early adult life as the number of moles increases up to the age of 30 to 40. BCC develops in cells of the skin called basal cells. SCC is the most accruing form of skin cancer after melanoma and usually results from exposure to UV rays
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