Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models
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Date
2023-09-13
Journal Title
Journal ISSN
Volume Title
Publisher
MPDI Bioengineering
Abstract
One of the most promising research initiatives in the healthcare field is focused on the
rising incidence of skin cancer worldwide and improving early discovery methods for the disease.
The most significant factor in the fatalities caused by skin cancer is the late identification of the
disease. The likelihood of human survival may be significantly improved by performing an early
diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from
the photographs of the tumors that may be used for the prospective identification of skin cancer.
Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis;
nevertheless, the literature demonstrates that there is still room for additional improvements in
various performance metrics. This study proposes a hybrid deep convolutional neural network
architecture for identifying skin cancer by adding two main heuristics. These include Xception and
MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer
learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been
detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains
the most excellent performance, particularly concerning the dataset that was evaluated: specifically,
it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55%
F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and
public health, offering a valuable tool for dermatologists and healthcare professionals in their fight
against skin cancer.
Keywords: skin cancer; deep convolutional neural network; transfer learning; data augmentation;
deep learning