Abstract
The Brain is the one that has a significant impact on the control and managing of the entire body. The ability to see, hear, think, walk, talk, feel, remember, and a lot more, and also the breathing which is the essential part to stay alive is controlled by the Brain. So it is a crucial part to take care of the brain from various diseases. Tumors, which are collections of abnormal growth of cells, can cause damage to the brain and can be malignant or non-cancerous. Here we are focusing on meningiomas, the majority of meningiomas are benign (non-cancerous) and slow-growing, although some are malignant. The detection of these types of tumors can be a daring task. As technology evolved, there are various methods that can detect brain tumors and even classify their types. The proposed work follows a hybrid feature extraction method that fuses PCA and GIST and also uses the SeaLion algorithm for optimization purposes. With the hybrid feature extraction techniques and the SLnO, the designed method shows a better classification accuracy. The paper includes the workflow of the proposed strategy, the first phase is all about the preprocessing of the image using the CLAHE and the anisotropic diffusion followed by the segmentation in the second phase, uses K-means, then the feature extraction in the third phase. The fourth phase deals with the optimization and finally the classification of inputs. The trials were conducted on 100 images from the human brain and a synthetic MRI dataset, with 25 images being healthy and 75 being problematic. On both training and test imagery, the classification performance was found to be 98.56%.
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Aswathy, S.U., Stephen, D., Vincent, B., Prajoon, P. (2022). A Hybrid Feature Extraction Method Using SeaLion Optimization for Meningioma Detection from MRI Brain Image. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_4
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DOI: https://doi.org/10.1007/978-3-030-96299-9_4
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