Classification of Primary and Secondary Brain Tumor Using Extreme Learning Machine

Rahmat, Romi Fadillah; Wijaya, Riyan Maria; Faza, Sharfina; Nababan, Erna Budhiarti; Nadi, Farhad; Al-Khowarizmi, Al-Khowarizmi

doi:10.1109/databia53375.2021.9650272

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…We can see 88% of accuracy from this study [16]. Other research shown great brain tumor segmentation using deep neural network [17], convolutional neural network (CNN) with 2-phase training and brain tumor image segmentation benchmark (BRATS) test data [18], CNN with 3x3 kernel [19] and many novel-adjusted CNN [20], [21], also CNN implementation in 3-dimentional MRI Scan [22], [23] and also extreme learning machine (ELM) [24].…”

Section: Introductionmentioning

confidence: 63%

Astrocytoma, ependymoma, and oligodendroglioma classification with deep convolutional neural network

Rahmat

Pratama

Purnamawati

et al. 2022

IJ-AI

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Glioma as one of the most common types of brain tumor in the world has three different classes based on its cell types. They are astrocytoma, ependymoma, oligodendroglioma, each has different characteristics depending on the location and malignance level. Radiological examination by medical personnel is still carried out manually using magnetic resonance imaging (MRI) medical imaging. Brain structure, size, and various forms of tumors increase the level of difficulty in classifying gliomas. It is advisable to apply a method that can conduct gliomas classification through medical images. The proposed methods were proposed for this study using deep convolutional neural network (DCNN) for classification with k-means segmentation and contrast enhancement. The results show the effectiveness of the proposed methods with an accuracy of 95.5%.

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Section: Introductionmentioning

confidence: 63%

Astrocytoma, ependymoma, and oligodendroglioma classification with deep convolutional neural network

Rahmat

Pratama

Purnamawati

et al. 2022

IJ-AI

Self Cite

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NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data

Haque,

Hassan,

Bairagi

et al. 2024

Sci Rep

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Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person’s life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model’s transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.

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Classification Of Aedes Mosquito Larva Using Convolutional Neural Networks And Extreme Learning Machine

Nainggolan,

Efendi,

Budiman

et al. 2023

2023 7th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)

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Classification of Primary and Secondary Brain Tumor Using Extreme Learning Machine

Cited by 3 publications

References 4 publications

Astrocytoma, ependymoma, and oligodendroglioma classification with deep convolutional neural network

Astrocytoma, ependymoma, and oligodendroglioma classification with deep convolutional neural network

NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data

Classification Of Aedes Mosquito Larva Using Convolutional Neural Networks And Extreme Learning Machine

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