Abstract
Among the various types of cancer disease, a brain tumor is one of the most common causes of death; because the cerebrum is an extremely delicate, perplexing, and focal part of the body. Existing skull stripping processes do not accurately segment the brain. On other hand, existing deep learning algorithms have class unevenness issues for the most part arise between solid tissue and growth tissue, just as between intra-tumoral classes. This problem causes sensible tissue to dominate during the initial phase, lowering the model's effectiveness. To overcome, this research implements the novel Brain and tumor segmentation using Histo-Quartic graph and stack entropy-based deep neural network method. Image pre-processing is finished through histogram equalization and median filtering approach. Segmentation of the brain may be a very vital step and the proposed approach is primarily based totally on Histo-Quartic graph segmentation with region-primarily based degree set function. The framework begins off evolving to section brain organ from Magnetic resonance imaging scan. Finally, tumor segmentation is the usage of stack entropy deep neural network. The experimental findings have our proposed approach outperforms the modern techniques in phrases of information.
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07 August 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s11063-024-11675-0
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Srividya, K., Anilkumar, B. & Sowjanya, A.M. RETRACTED ARTICLE: Histo-Quartic Graph and Stack Entropy-Based Deep Neural Network Method for Brain and Tumor Segmentation. Neural Process Lett 55, 7603–7625 (2023). https://doi.org/10.1007/s11063-023-11276-3
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DOI: https://doi.org/10.1007/s11063-023-11276-3