Application of Improved Chameleon Swarm Algorithm and Improved Convolution Neural Network in Diagnosis of Skin Cancer

Skin cancer is affected by the uncommon evolution of skin cells and a deadly type cancer. In addition, lesion numerous factors, such as exposure to sun, infections, allergies, etc. These illnesses have become challenge in therapeutic diagnosis because virtual resemblances, where image classification vital sufficiently diagnose dissimilar lesions. Therefore, early significant can avert cancers like focal cell carcinoma melanoma. A deep learning-based computer analyzing model be an automatic solution medical evaluations overcome this issue. Hence, paper suggests improved chameleon swarm algorithm convolutional neural networks (ICSA-CNN) for effective identification classification. The data are collected from Kaggle dataset classifying Chameleon clustering technique utilized mining cluster utilizing dynamic systems, it resolve constrained global numerical optimization issues detection.