Abstract
Skin cancer is usually classified as melanoma and non-melanoma. Melanoma now represents 75% of humans passing away worldwide and is one of the most brutal types of cancer. Previously, studies were not mainly focused on feature extraction of Melanoma, which caused the classification accuracy. However, in this work, Histograms of orientation gradients and local binary patterns feature extraction procedures are used to extract the important features such as asymmetry, symmetry, boundary irregularity, color, diameter, etc., and are removed from both melanoma and non-melanoma images. This proposed Efficient Classification Systems for the Diagnosis of Melanoma (ECSDM) framework consists of different schemes such as preprocessing, segmentation, feature extraction, and classification. We used Machine Learning (ML) and Deep Learning (DL) classifiers in the classification framework. The ML classifier is Naïve Bayes (NB) and Support Vector Machines (SVM). And also, DL classification framework of the Convolution Neural Network (CNN) is used to classify the melanoma and benign images. The results show that the Neural Network (NNET) classifier’ achieves 97.17% of accuracy when contrasting with ML classifiers.
Highlights
Melanoma is a typical type of cancer that occurs on the skin
The results show that the Neural Network (NNET) classifier’ achieves 97.17% of accuracy when contrasting with Machine Learning (ML) classifiers
The dermoscopic Melanoma and benign images are downloaded from the Kaggle database, which is initially preprocessed by using the Bottom Hat Filter (BHF) algorithm—and preprocessing images sent to the segmentation scheme, feature extraction, and classification process
Summary
Melanoma is a typical type of cancer that occurs on the skin. It starts with pigment melanin-forming cells which are responsible for melanocytes-skin shading. Some people with pulmonary hair loss are probably unable to find anatomical structures due to overuse, for example, ribs with too many alkaline bones. CNN [14] has been presented in this area, and their models have been broadly acknowledged for including extraction and prompting for improved classification [15]. In such arrangements, deep discriminant features are removed by different layers as pooling, convolution, and feed-forward layers from the images by implanting an idea of Transfer Learning using tweaking and features descriptors. The CNN models improve the determination framework execution [16,17,18,19]
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