An improved whale optimization algorithm-based radial neural network for multi-grade brain tumor classification

Dixit, Asmita; Nanda, Aparajita

doi:10.1007/s00371-021-02176-5

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…The main objective of this study is to illustrate the explainability capabilities of our proposed NeuroXAI framework for assisting clinicians, not to obtain the best classification results only. However, the applied classifier achieved a superior accuracy of 98.62%, comparing to the state-of-the-art methods [ 8 , 13 – 16 ] as given in Table 2 .…”

Section: Resultsmentioning

confidence: 92%

“…However, the applied classifier achieved a superior accuracy of 98.62%, comparing to the state-of-the-art methods [ 8 , 13 – 16 ] as given in Table 2 .…”

Section: Resultsmentioning

confidence: 92%

“…For instance, U-Net variants [ 11 , 12 ], which use symmetric encoder–decoder architecture, have performed state-of-the-art results for medical image segmentation. Similarly, several publications have appeared in recent years for accurate medical classification including [ 8 , 13 – 16 ]. Hence, employing DL technologies in CADs could potentially expand physicians’ capabilities assisting in perioperative evaluation of intracranial pathologies and enhancing the efficiency of postoperative follow-up [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Explainability of deep neural networks for MRI analysis of brain tumors

et al. 2022

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Purpose Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice. Methods In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent. Results NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN. Conclusion Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI.

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Section: Resultsmentioning

confidence: 92%

“…However, the applied classifier achieved a superior accuracy of 98.62%, comparing to the state-of-the-art methods [ 8 , 13 – 16 ] as given in Table 2 .…”

Section: Resultsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Explainability of deep neural networks for MRI analysis of brain tumors

et al. 2022

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“…This table clearly reveals the superiority of the introduced scheme based on segmentation accuracy. The existing methods which are compared with the proposed method are: HCNNs, 40 Improved Whale Optimization Algorithm‐Based Radial Neural Network, 42 RepOptimizer, 41 Exponentially Weighted Pelican Chimp Optimization‐based Shepard, 43 U‐Net, 19 transformer‐enhanced convolutional neural network (TECNN), 44 ResUNet++, 45 JGate‐Attention based ResUNet, 46 UNet, 47 self‐supervised learning (SSL), 48 ResUNet Segmentation (ResUNet‐SEG), 49 HDNN, 50 MHKFC 51 and DenseUNet+ 52 . The accuracy percentages ranged from 79.9% (U‐Net‐based models) to 96.75% (TECNN) for the BraTS 2018 dataset and the introduced method attained segmentation accuracy of 97.5%.…”

Section: Experimental Outcomesmentioning

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

Int J Imaging Syst Tech

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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.

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“…Dixit and Nanda 79 proposed multigrade brain tumor classification using an improved whale optimization algorithm (IWOA) and radial basis neural network (RBNN). At first, the preprocessing of MRI images is initiated, and then FCM clustering‐based segmentation is employed for tumor area identification.…”

Section: Optimization Algorithms For Disease Identificationmentioning

confidence: 99%

A review on optimization techniques for medical image analysis

Kaur

Singh²

2022

Concurrency and Computation

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Data mining of medical imaging approaches makes it difficult to determine their value in the disease's insight, analysis, and diagnosis. Image classification presents a significant difficulty in image analysis and plays a vital part in computer-aided diagnosis. This task concerned the use of optimization techniques for the utilization of image processing, pattern recognition, and classification techniques, as well as the validation of image classification results in medical expert reports. The primary intention of this study is to analyze the performance of optimization techniques explored in the area of medical image analysis. For this motive, the optimization techniques employed in existing literature from 2012 to 2021 are reviewed in this study. The contribution of optimization-based medical image classification and segmentation utilized image modalities, data sets, and tradeoffs for each technique are also discussed in this review study. Finally, this review study provides the gap analysis of optimization techniques used in medical image analysis with the possible future research direction.

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An improved whale optimization algorithm-based radial neural network for multi-grade brain tumor classification

Cited by 16 publications

References 31 publications

Explainability of deep neural networks for MRI analysis of brain tumors

Explainability of deep neural networks for MRI analysis of brain tumors

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

A review on optimization techniques for medical image analysis

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