Abstract
Today's technologies have deeply influenced human health and daily life. Consequently, the health care process is widely improved to automate and detect diseases. Deep Learning and Transfer Learning classifiers are within emergent technologies that impact health care. In this paper, we used Transfer Learning and Convolutional Neural Network to classify and detect the Intracranial hemorrhage (ICH). The performance of the used classifiers is evaluated and compared on the Intracranial Hemorrhage Dataset that contains 2814 images. The results show that the detection accuracy of Transfer Learning with Inception V3, which achieves 88.97%, is superior to that of the Convolutional neural network.
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El Mouna, L., Silkan, H., Haynf, Y., Tmiri, A., Dahmouni, A. (2022). Comparative Study of Deep Learning Models for Detection and Classification of Intracranial Hemorrhage. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_10
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DOI: https://doi.org/10.1007/978-3-031-06458-6_10
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