Abstract
With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most skin diseases have similar features, which make it challenging for dermatologists to diagnose patients accurately. Therefore, machine and deep learning techniques can have a critical role in diagnosing dermatoscopy images and in the accurate early detection of skin diseases. In this study, systems for the early detection of skin lesions were developed. The performance of the machine learning and deep learning was evaluated on two datasets (e.g., the International Skin Imaging Collaboration (ISIC 2018) and Pedro Hispano (PH2)). First, the proposed system was based on hybrid features that were extracted by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and wavelet transform (DWT). Such features were then integrated into a feature vector and classified using artificial neural network (ANN) and feedforward neural network (FFNN) classifiers. The FFNN and ANN classifiers achieved superior results compared to the other methods. Accuracy rates of 95.24% for diagnosing the ISIC 2018 dataset and 97.91% for diagnosing the PH2 dataset were achieved using the FFNN algorithm. Second, convolutional neural networks (CNNs) (e.g., ResNet-50 and AlexNet models) were applied to diagnose skin diseases using the transfer learning method. It was found that the ResNet-50 model fared better than AlexNet. Accuracy rates of 90% for diagnosing the ISIC 2018 dataset and 95.8% for the PH2 dataset were reached using the ResNet-50 model.
Highlights
The skin is the largest human organ, and it is the outer covering of the body
The training process for the artificial neural network (ANN) and feedforward neural network (FFNN) algorithms is described. These algorithms consisted of an input layer with 220 neurons, 10 hidden layers in which all the computations were performed, and an output layer containing seven classes for the ISIC 2018 dataset and three categories for the PH2 dataset
An automated learning system was developed based on segmentation methods, separating the lesion area from healthy skin and extracting the hybrid characteristics using three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and DWT
Summary
The skin is the first line of defense in the human body [1]. It has the role of (i) protecting the internal organs from external environmental influences, (ii) regulating body temperatures, (iii) providing immunity against many diseases, and (iv) providing beauty to the body [2]. The human body is protected by the skin from harmful ultraviolet rays from the sun, the essential vitamin D is produced by this organ when the body is exposed to sunlight. Cellular DNA is damaged if the body is exposed to sunlight or ultraviolet rays for a long time, decreasing skin pigmentation and the incidence of malignant skin diseases. Abnormal cancerous cells divide rapidly, penetrating the lower skin layers and becoming incurable malignant melanomas [4]
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