Abstract
A deep learning network is super-efficient for image processing. The artificial brain of the deep learning system is highly capable of solving the categorization of problems. However, deep learning has several restrictions on the intelligence of the human brain. The human brain can update and improve knowledge by analyzing the features of images. In the artificial brain, the arrangements of the pixels are used for image understanding. This pixel-based continuous learning tendency of the artificial brain of deep learning leading to reducing the knowledge of previously achieved relevant information of the system is called the catastrophic forgetting problem of deep learning. Recently, many researchers reported that the catastrophic forgetting problem affects the result of deep learning. So, in this study, we are providing an excellent solution for the catastrophic forgetting problem of deep learning by restricting the direct analysis of input images with image morphological features. This image morphological feature-based deep learning system helps the artificial brain to concentrate on image features. This image morphological features system-based study achieved a stable learning graph with 80% average learning accuracy.
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Thanikkal, J.G., Dubey, A.K. & Thomas, M.T. A deep-feature based estimation algorithm (DFEA) for catastrophic forgetting. J Ambient Intell Human Comput 14, 16771–16784 (2023). https://doi.org/10.1007/s12652-023-04686-7
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DOI: https://doi.org/10.1007/s12652-023-04686-7