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Deep Convolutional Neural Networks for Brain Tumor Image Segmentation

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Proceedings of Second International Conference on Intelligent System (ICIS 2023)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Brain segmentation approaches based off deep learning are commonly utilized because of the lack of ideality of the methods which have their base in classical image processing and machine learning. A classic reference of the same is the deep convolutional network model, which suffers from an enormous number of boundaries and significant information loss during encoding and decoding, despite the strong brain segmentation characteristic of convolutional network model in the deep learning category. Hence, this article comes forward with a deep convolutional neural network fusion support vector machine algorithm. The proposed segmentation model for brain tumors is split into three stages. The initial stage involves the training of a deep convolutional neural network to become proficient in the mapping of tumor marker space from image space. The projected labels from the deep convolutional neural network, as well as the test images, are fed into the support vector machine classifier in the subsequent stage. The final stage involves training a deep classifier by linking in series an embedded support vector machine to the deep convolutional neural network. Segmentation of brain tumors can be done by running individual models on the BraTS dataset in addition to the human fabricated dataset. The segmentation findings show that the suggested model outperforms the integrated SVM classifier and the deep convolutional neural network.

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Correspondence to Masood Ahamed Shaik .

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Shaik, M.A., Jabez, J. (2024). Deep Convolutional Neural Networks for Brain Tumor Image Segmentation. In: Tavares, J.M.R.S., Pal, S., Gerogiannis, V.C., Hung, B.T. (eds) Proceedings of Second International Conference on Intelligent System. ICIS 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8976-8_1

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