RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Brain tumor segmentation (BTS) from magnetic resonance imaging (MRI) scans is crucial for the diagnosis, treatment planning, and monitoring of therapeutic results. Thus, this research work proposes a novel graph momentum fully convolutional network with a modified Elman spike neural network (MESNN) for BTS and overall survival prediction (OSP). Initially, the introduced graph momentum fully convolutional network segments the brain tumor as enhanced tumor, the tumor core, and the whole tumor from the pre‐processed MRI scans. Second, the texture, intensity, shape, and wavelet features were extracted from the segmented tumors. Then, the horse herd optimization algorithm is utilized to minimize the feature's dimensionality. Finally, the OSP is performed by the MESNN which classifies the survival prediction of a patient as long‐term, mid‐term, and short‐term. The achieved segmentation accuracy of proposed method is 97% and the survival prediction's average RMSE is 215.5.

Section: Related Workmentioning

confidence: 99%

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

Ramkumar,

Kumar,

Padmapriya

et al. 2024

“…Zhu et al 27 proposed a new automatic classification model (RBEBT) of brain tumors using fine‐tuned ResNet18 to extract the features of brain tumor images. The RNN is selected as the classifier and the bat algorithm is selected to optimize the parameters of RNN.…”

Section: Related Workmentioning

confidence: 99%

Brain tumor image segmentation based on prior knowledge via transformer

Li,

Liu,

Nie

et al. 2023

Many researchers use AI to improve the accuracy of early diagnostic techniques. However, as a result of the tumor's uneven shape, fuzzy borders and too few data, existing tumor segmentation methods do not propose accurate segmentation results. We innovative introduces the prior knowledge learned to filter the noise information and guide the final network to generate a more accurate segmentation model. First, we introduce a classification network with an attention block to highlight the potential location of the brain tumor and also obtain the rough diagnosis result as the prior knowledge. Second, we provide a novel image fusion network consisting of a transformer with cross attention to merge tumor localization information with brain MRI images. Third, we propose a novel multilayer transformer experience information fusion network to combine the classic U‐Net network to handle the guiding of prior knowledge. The higher performance of the suggested method is demonstrated by comparison with contemporary methods.

“…The authors tested the proposed optimization technique on BraTS MRI datasets to demonstrate the efficacy of the algorithm. [33][34][35][36][37][38] BraTS is a collection of MR brain tumour images, especially high-and LGGs in multiple modalities including T1, T2, T1-C, and T2-FLAIR. BraTS images are collected from various organisations at benchmark clinical manifestations, as well as using specialised equipment and imaging studies, resulting in widely varying image quality indicating diverse medical procedures.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Glioma detection using EHO based FLAME clustering in MR brain images

Karun,

Prasath,

Rajasekaran

et al. 2023

MRI is a popular imaging method for examining brain tumours. The ability to precisely segment tumours from MRI is absolutely essential for medical diagnostics and surgical planning. Manual tumour segmentation might be unrealistic for more comprehensive studies. Deep learning is the most widely used technique in medical diagnosis. For effective tumour dissection from brain MRI, this paper proposed a novel combination of FLAME and EHO Algorithm. FLAME is a type of clustering method that groups the most similar pixels in to a single cluster. EHO algorithm is one of the nature‐inspired metaheuristic optimization algorithms based on the social herding behaviour of elephants and swimming search methods. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The average computational time, mean squared error, peak signal to noise ratio, tanimoto coefficient, and dice score ‐ obtained are 23.3775 s, 0.213, 54.9669 dB, 54.6148%, and 84.053%, respectively.