Convolutional neural network-based MRI brain tumor classification system

Amanullah, M.; Visumathi, J.; Sammeta, Naresh; Ashok, M.

doi:10.1063/5.0109770

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In addition, vision transformer (ViT)-based methods were presented relatively recently to overcome the problem of long-range dependence in CNNs. Amanullah et al [ 21 ] showed that manual visual training for image identification may result in error detection and may be circumvented by machine learning’s most popular job. In this study, the convolutional neural network (CNN) model was developed using data augmentation and image processing methods in order to categorize the brain MRI scan images as malignant or non-cancerous and identify the different kinds of brain tumors.…”

Section: Related Workmentioning

confidence: 99%

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

et al. 2023

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To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.

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Section: Related Workmentioning

confidence: 99%

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

et al. 2023

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“…As the brain is a vital organ responsible for cognitive function, the presence of tumors can have life-threatening consequences. Brain tumors account for 85–90% of all central nervous system tumors, according to a report [ 2 ]. Radiologists use imaging techniques such as CT scans and MRI to locate cancer in the brain, with MRI providing higher-resolution imaging than CT scans.…”

Section: Introductionmentioning

confidence: 99%

A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding

et al. 2023

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A brain tumor refers to an abnormal growth of cells in the brain that can be either benign or malignant. Oncologists typically use various methods such as blood or visual tests to detect brain tumors, but these approaches can be time-consuming, require additional human effort, and may not be effective in detecting small tumors. This work proposes an effective approach to brain tumor detection that combines segmentation and feature fusion. Segmentation is performed using the mayfly optimization algorithm with multilevel Kapur’s threshold technique to locate brain tumors in MRI scans. Key features are achieved from tumors employing Histogram of Oriented Gradients (HOG) and ResNet-V2, and a bidirectional long short-term memory (BiLSTM) network is used to classify tumors into three categories: pituitary, glioma, and meningioma. The suggested methodology is trained and tested on two datasets, Figshare and Harvard, achieving high accuracy, precision, recall, F1 score, and area under the curve (AUC). The results of a comparative analysis with existing DL and ML methods demonstrate that the proposed approach offers superior outcomes. This approach has the potential to improve brain tumor detection, particularly for small tumors, but further validation and testing are needed before clinical use.

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A framework for brain tumor detection based on segmentation and features fusion using MRI images

et al. 2023

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Convolutional neural network-based MRI brain tumor classification system

Cited by 3 publications

References 11 publications

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding

A framework for brain tumor detection based on segmentation and features fusion using MRI images

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