Brain Tumor Segmentation to Calculate Percentage Tumor Using MRI

Wulandari, Annisa; Sigit, Riyanto; Bachtiar, Mochamad Mobed

doi:10.1109/kcic.2018.8628591

Cited by 40 publications

(11 citation statements)

References 1 publication

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“…Further we can speculate that estimation of progress of brain cancer from 3D MRI scans can be performed 16 using hybrid quantum neural networks, which is an ongoing research.…”

Section: Accuracy =mentioning

confidence: 99%

Detection of brain cancer using quantum-classical CNN based method

Sriya¹,

Warrier²,

Dontabhaktuni³

2023

Quantum Computing, Communication, and Simulation III

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Classification of MRI images of brain cancer using deep learning methods such as CNN's is an increasingly popular method to detect cancer and its spread. In the present work, we perform a competitive analysis of hybrid quantum CNN based methods to classify the MRI images of brain cancer into three different classes using quantum simulators. The quantum image processing is done via three encoding schemes, viz. QCNN, FRQI and NEQR. We see that QCNN has higher accuracy of 87% with a precision of 81%. The NEQR and FRQI encoding schemes have an accuracy of 79% and 75%, respectively. The computational time for QCNN, FRQI and NEQR are considerably less than the conventional CNN method, which was tested by running the same dataset through DenseNet121 Keras architecture.

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“…Further we can speculate that estimation of progress of brain cancer from 3D MRI scans can be performed 16 using hybrid quantum neural networks, which is an ongoing research.…”

Section: Accuracy =mentioning

confidence: 99%

Detection of brain cancer using quantum-classical CNN based method

Sriya¹,

Warrier²,

Dontabhaktuni³

2023

Quantum Computing, Communication, and Simulation III

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“…The exact mpMRI scans included four types are: -Fluid Attenuated Inversion Recovery (FLAIR) -T1-weighted pre-contrast (T1w) -T1-weighted post-contrast (T1Gd) -T2-weighted (T2) This dataset is seperated in two labels are 0 and 1 for the NGMT value, which is the diagnosis scale of Brain-Tumour Detection. [15] Fig. 1.…”

Section: Datasetmentioning

confidence: 99%

EfficientNet for Brain-Lesion Classification

Trinh

Mau²,

Zosimov³

et al. 2022

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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In the development of technology, there are increasing cases of brain disease, there are more treatments proposed and achieved a positive result. However, with Brain-Lesion, the early diagnoses can improve the possibility for successful treatment and can help patients recuperate better. From this reason, Brain-Lesion is one of the controversial topics in medical images analysis nowadays. With the improvement of the architecture, there is a variety of methods that are proposed and achieve competitive scores. In this paper, we proposed a technique that uses efficient-net for 3D images, especially the Efficient-net B0 for BrainLesion classification task solution, and achieve the competitive score. Moreover, we also proposed the method to use Multiscale-EfficientNet to classify the slices of the MRI data.

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“…These units are interconnected in each layer with different weight connections. [9] The filter Sobel determines the image edges by locating by-products of images. It just looks for gradients in the x and y directions.…”

Section: Algorithmmentioning

confidence: 99%

Anisotropic Filtering Algorithm in Image Processing Technique for the Identification of Tumour from Mri

2022

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Brain Tumor Segmentation to Calculate Percentage Tumor Using MRI

Cited by 40 publications

References 1 publication

Detection of brain cancer using quantum-classical CNN based method

Detection of brain cancer using quantum-classical CNN based method

EfficientNet for Brain-Lesion Classification

Anisotropic Filtering Algorithm in Image Processing Technique for the Identification of Tumour from Mri

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