Abdel-Zaher, MohamedHisham, MustafaYousri, RetajDarweesh, M. Saeed2021-09-112021-09-112021-07https://doi.org/10.1109/ICEEM52022.2021.9480378https://qrgo.page.link/s8SnXScopusFire disasters damage the economy across the globe and cause many casualties among civilians and firefighters. In this paper, a deep learning architecture based on the convolutional neural network (CNN) is proposed to detect fires efficiently. We trained the network on 9247, picked high-resolution images containing fire and other ones without any fire, and investigated the effect of CNN depth on its classification accuracy. In this proposed work, we achieved 98% accuracy on the testing set, which is so far better than the previous state-of-the-art and will eventually minimize fire disasters and reduce the damage caused by human resources. © 2021 IEEE.en-USClassificationComputer VisionConvolution Neural Network (CNN)Fire DetectionSupervised LearningArticlehttps://doi.org/10.1109/ICEEM52022.2021.9480378