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

Abstract

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