Optimization Convolutional Neural Network for Automatic Skin Lesion Diagnosis Using a Genetic Algorithm

Examining and predicting skin cancer from lesion images is challenging due to the complexity of images. Early detection treatment disease can prevent mortality as it be curable. Computer-aided diagnosis (CAD) provides a second opinion for dermatologists they classify type with high accuracy their ability show various clinical identification features locally globally. Convolutional neural networks (CNNs) have significantly improved performance CAD systems medical image segmentation classifications. However, tuning CNNs are since search space all possible hyperparameter configurations substantially vast. In this paper, we adopt genetic algorithm automatically configure CNN model an accurate, reliable, robust automated classification early diagnosis. The optimized uses four public datasets train able detect abnormalities based on in different orientations. achieves best scores each DICE coefficients, precision measure, F-score. These compare better than other existing methods. Considering success model, could valuable method implement settings.