Skin Cancer Identification using Cat Swarm-Intelligent Generative RNN Algorithm
Keywords:
Skin cancer, clinical images, Weiner filter (WF), Gabor filter bank (GFB), cat swarm-intelligent generative recurrent neural network (CS-IGRNN)Abstract
Cancer of the skin is one type of cancer that begins in the skin itself. It occurs when the growth of abnormal skin cells becomes unchecked. Sunlight and artificial tanning sources are the leading causes of skin cancer. However, additional elements, including genetics, a compromised immune system, and exposure to specific toxins, can also influence how it develops. This study suggests utilizing the cat swarm-intelligent generative recurrent neural network (CS-IGRNN) method to identify skin cancer. This research can aid in the early detection and successful treatment of skin cancer. We used an overall total of 22,000 clinical image datasets gathered from the Dermquest and DermIS Digital Databases. The Weiner filter (WF) is used in image preprocessing to eliminate the captured raw images. Gabor filter bank (GFB) processing extracts the features from the enhanced image. Using a CS-IGRNN to categorize cancer images has been proposed as a potential remedy. RNN, one of the cat’s swarm-intelligent generating algorithms, provides precise information about pictures and achieves incredibly good outcomes in image classification. Accuracy, precision, f1-score, specificity, and sensitivity are utilized to evaluate the efficacy of the proposed method. It compared to other methods, extensive testing shows that ours is the most effective.
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