Abstract:
Skin cancer these days have become quite a common occurrence especially in
certain geographic areas such as Oceania. Early detection of such cancer with
high accuracy is of utmost importance, and studies have shown that deep
learning- based intelligent approaches to address this concern have been
fruitful. In this research, we present a novel deep learning- based classifier
that has shown promise in classifying this type of cancer on a relevant
preprocessed dataset having important features pre-identified through an
effective feature extraction method.
Skin cancer in modern times has become one of the most ubiquitous types of
cancer. Accurate identification of cancerous skin lesions is of vital importance
in treating this malady. In this research, we employed a deep learning approach
to identify benign and malignant skin lesions. The initial dataset was obtained
from Kaggle before several preprocessing steps for hair and background
removal, image enhancement, selection of the region of interest (ROI),
region-based segmentation, morphological gradient, and feature extraction
were performed, resulting in histopathological images data with 20 input
features based on geometrical and textural features. A principle component
analysis (PCA)-based feature extraction technique was put into action to
reduce the dimensionality to 10 input features. Subsequently, we applied our
deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately
at a very early stage. The highest accuracy was obtained with the Adamax
optimizer with a learning rate of 0.006 from the neural network-based model
developed in this study. The model also delivered an impressive accuracy of
approximately 99.19%.