Abstract
One of the deadliest diseases is skin cancer, especially melanoma. The high resemblance between different skin lesions such as melanoma and nevus in the skin colour images increases the complexity of identification and diagnosis. An efficient automated early detection system for skin cancer detection is essential in order to save human lives, time, and effort. In this article, an automatic skin lesion classification system using a pretrained deep learning network and transfer learning was proposed. Here, diagnosing melanoma in premature stages, a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy images of skin were taken and this is subjected to a preprocessing step for noise removal and postprocessing step for image enhancement. Then the processed image undergoes image segmentation using k-means and modified k-means clustering. Second, using feature extraction technology, Gray Level Co-occurrence Matrix, and first order statistics, characteristics are extracted. Features are selected on the basis of Harris Hawks optimization (HHO). Finally, various classifiers are used for predicting the stages and efficiency of the proposed work. Measures of well-known quantities, sensitivity, precision, accuracy, and specificity are used in assessing the efficiency of the suggested method, where higher values were obtained. Compared to the current methods, it is found that the classification rate exceeded the output of the current approaches in the performance of the proposed approach.
Highlights
One of the deadliest diseases that currently afflict humankind is cancer
(viii) Error evaluation: on the basis of the fitness function, better solutions are obtained and that solution yields less MSE and MSE is calculated as follows: MSerr 1/gΣe 1g[Fg − Fg′]2 (ix) Weight update (x) Error recomputation: errors are recalculated with the help of formula in fully connected layer and those generating less error are given to convolutional neural network (CNN) training (xi) Optimum weights are determined: for each solution, errors are calculated and those having less errors are given to training process (xii) Termination
For proper identification of skin texture, hairs in the skin are to be extracted, and for that top-hat transform is used. e image after hair removal is given into the ADFUSM to remove the noise. e image after noise removal is Methods Image 1
Summary
One of the deadliest diseases that currently afflict humankind is cancer. Skin cancer is one of the most common and the deadliest among all types of cancer. Melanoma is the dangerous skin cancer, if it is detected in the early stages, it can be curable, but progressive melanoma is deadly. Skin cancer (squamous cell and basal cell, melanoma and malignant) is rare in children. When melanomas occur, they normally arise from pigmented nevi (moles) which are (diameter
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.