Abstract
When skin cells divide abnormally, it can cause a tumor or abnormal lymph fluid or blood. The masses appear benign and malignant, with the benign being limited to one area and not spreading, but some can spread throughout the body through the body’s lymphatic system. Skin cancer is easier to diagnose than other cancers because its symptoms can be seen with the naked eye. This makes us to provide an artificial intelligence-based methodology to diagnose this cancer with higher accuracy. This article proposes a new non-destructive testing method based on the AlexNet and Extreme Learning Machine network to provide better results of the diagnosis. The method is then optimized based on a new improved version of the Grasshopper optimization algorithm (GOA). Simulation of the proposed method is then compared with some different state-of-the-art methods and the results showed that the proposed method with 98% accuracy and 93% sensitivity has the highest efficiency.
Highlights
The skin is the protective layer of the body that covers it all around and protects us from sunlight, heat, cold, superficial damage such as wounds and scratches, infection, and penetration of bacteria and viruses
Skin cancer is easier to diagnose than other cancers because its symptoms can be seen with the naked eye
The results showed that developed deep Convolution Neural Network (CNN) had a better ability to detect damaged areas of the skin and melanoma spots compared to classical methods
Summary
The skin is the protective layer of the body that covers it all around and protects us from sunlight, heat, cold, superficial damage such as wounds and scratches, infection, and penetration of bacteria and viruses. K-Fuzzy C- a technique based on the development of the RFO algorithm provided more accurate and reliable results in the classification of the skin and the detection of melanoma spots on the skin. The results showed that developed deep CNN had a better ability to detect damaged areas of the skin and melanoma spots compared to classical methods. Deep learning optimized by WHO algorithm was used to detect melanoma spots on the skin. Diagnosis and classification of skin cancer in the early stages of development can increase the possibility of recovery of patients For this purpose, the CNN was used to distinguish benign from malignant spots. This study uses a hybrid technique based on deep learning and metaheuristics for the diagnosis of skin cancer. The new metaheuristic is based on an improved version of the Grasshopper optimization algorithm (GOA) which provides results with higher accuracy and precision
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