An Interactive Machine Learning Approach for Brain Tumor MRI Segmentation

Dash, Sachikanta; Das, Rajendra Kumar; Guha, Subhankar; Bhagat, Surendra Nath; Behera, G. K.

doi:10.1007/978-981-16-0695-3_38

Cited by 4 publications

(1 citation statement)

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“…Simulation results of the proposed method have been compared with those of eight latest existing works (as seen in Table 8 ). Noise is eliminated from the MRI slices with an anisotropic filter, and we classify the tumor/healthy slices using SVM, which provides 96.04% prediction scores [ 70 ]. A nonlocal mean filter is used for noise reduction, and tumor pixels are segmented by region growth.…”

Section: Resultsmentioning

confidence: 99%

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Amin

Anjum

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.

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Section: Resultsmentioning

confidence: 99%