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

Rahmat, Romi Fadillah; Pratama, Mhd Faris; Purnamawati, Sarah; Faza, Sharfina; Lubis, Arif Ridho; Al-Khowarizmi, Al-Khowarizmi; Lubis, Muharman

doi:10.11591/ijai.v11.i4.pp1306-1313

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…We employ the ReLU activation function for the convolutional layer and the Softmax for the output layer to obtain results for categorical data. This work uses root mean square propagation (RMSProp) to help the optimizer understand where the optimization should be made inside the DCNN [30].…”

Section: Setup In the Environment Of A Deep Convolutional Neural Netw...mentioning

confidence: 99%

Proportion Estimation and Multi-Class Classification of Abnormal Brain Cells

Joshi,

Singh

2024

MEDIN

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Vagueness in the determination of the tumor size creates significant hindrances in planning and quantitatively assessing brain tumor treatments. Non-invasive magnetic resonance imaging has become a primary non-ionizing radiation diagnostic tool for brain cancers. It takes a long time to manually segment the extent of a brain tumor from 3D M.R.I. volumes, and the performance heavily depends on the operator's skill. A precise and automated brain tumor segmentation tool is needed desperately. In this case, an accurate assessment of the tumor's extent requires a reliable automated segmentation method for the brain tumor. The Multimodal Brain Tumor Image Segmentation (BRATS 2020) dataset is used in this paper to demonstrate an automated deep convolutional network, or U-Net, method for brain tumor segmentation. Deep Learning and Transfer Learning are utilized to improve the accuracy and effectiveness in detecting and recognizing different types of brain cancers. The unobserved images' F1 scores were 98% and 99%, respectively.

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Section: Setup In the Environment Of A Deep Convolutional Neural Netw...mentioning

confidence: 99%

Proportion Estimation and Multi-Class Classification of Abnormal Brain Cells

Joshi,

Singh

2024

MEDIN

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“…Notably, the depth of the layers in a deep learning model directly correlates with the complexity of the data representations it can learn. This makes deep learning particularly effective when managing large and complex datasets, such as images, sounds, and text [30,31].…”

Section: Deep Learningmentioning

confidence: 99%

Improving Spell Checker Performance for Bahasa Indonesia Using Text Preprocessing Techniques with Deep Learning Models

Lubis,

Lase,

Rahman

et al. 2023

ISI

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Spell checking capabilities, crucial within the domain of natural language processing, often encounter limitations in the context of Bahasa Indonesia due to data irregularities and the scarcity of high-quality training data. This study aims to enhance spell checker performance through the implementation of various text preprocessing techniques, including case folding, tokenization, stemming, and the removal of stop words. A Convolutional Neural Network (CNN), a deep learning model, was employed in this research to facilitate the overall process. The study utilized data gathered from social media communities, comprising a total of 10,000 entries. This data was divided into two subsets; 80% (8,000 entries) was allocated for training and the remaining 20% (2,000 entries) was designated for testing. A series of tests were conducted on datasets subject to different preprocessing approaches: without case folding, without stop words removal, without stemming, and with all text preprocessing stages implemented. The evaluation metrics employed in this study included accuracy, recall, precision, and the F1 score. The results demonstrated notable improvements in spell checker performance with appropriate text preprocessing. Specifically, the accuracy reached 0.86 for the dataset without stemming, 0.74 for the dataset without stop words removal, 0.7 for the dataset without case folding, and 0.89 for the dataset where all preprocessing stages were applied. These findings suggest that a comprehensive text preprocessing approach, paired with deep learning models, can significantly enhance spell checker performance for Bahasa Indonesia.

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“…A glial tumour is a tumour that originates in the glial cells. The glial cells are the glue-like cells responsible for supporting and protecting neurons [4]. A non-glial tumour is a tumour that starts to arise from cells in the brain, which are not categorized as glial cells.…”

Section: Introductionmentioning

confidence: 99%

An introduction to double stain normalization technique for brain tumour histopathological images

Akmal Dzulkifli,

Yusoff Mashor,

A. Raof

et al. 2024

IJECE

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Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses.

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

Cited by 5 publications

References 18 publications

Proportion Estimation and Multi-Class Classification of Abnormal Brain Cells

Proportion Estimation and Multi-Class Classification of Abnormal Brain Cells

Improving Spell Checker Performance for Bahasa Indonesia Using Text Preprocessing Techniques with Deep Learning Models

An introduction to double stain normalization technique for brain tumour histopathological images

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