Brain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural Network

Dehkordi, Amin Abdollahi; Hashemi, Mina; Neshat, Mehdi; Mirjalili, Ali; Sadiq, Ali Safaa

doi:10.2139/ssrn.4292650

Cited by 13 publications

(3 citation statements)

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“…Grid search, statistical-based optimization algorithms, and other heuristics were also used to detect brain tumor types. The following are used in brain tumor classification studies: Bayesian optimization algorithm [43], grid search [44], Nonlinear Lévy Chaotic Moth Flame Optimizer (NLCMFO) [45], Combined Political Optimizer [46], Improved Political Optimizer [47], Genetic Algorithm (GA) [48].…”

Section: Related Workmentioning

confidence: 99%

“…When the existing studies are examined, there are many studies using scratch models [19][20][21][22][23][24][25], transfer learning [26][27][28][29][30][31][32][33][34][35][36], ensemble learning [1,[37][38][39][40][41][42], and different optimization algorithms [4,[43][44][45][46][47][48][49][50][51][52][53][54]. Table 1 summarizes related studies on brain tumor classification in terms of method, dataset, classification type, and results.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging

Çetin-Kaya,

Kaya

2024

Diagnostics

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Brain tumors can have fatal consequences, affecting many body functions. For this reason, it is essential to detect brain tumor types accurately and at an early stage to start the appropriate treatment process. Although convolutional neural networks (CNNs) are widely used in disease detection from medical images, they face the problem of overfitting in the training phase on limited labeled and insufficiently diverse datasets. The existing studies use transfer learning and ensemble models to overcome these problems. When the existing studies are examined, it is evident that there is a lack of models and weight ratios that will be used with the ensemble technique. With the framework proposed in this study, several CNN models with different architectures are trained with transfer learning and fine-tuning on three brain tumor datasets. A particle swarm optimization-based algorithm determined the optimum weights for combining the five most successful CNN models with the ensemble technique. The results across three datasets are as follows: Dataset 1, 99.35% accuracy and 99.20 F1-score; Dataset 2, 98.77% accuracy and 98.92 F1-score; and Dataset 3, 99.92% accuracy and 99.92 F1-score. We achieved successful performances on three brain tumor datasets, showing that the proposed framework is reliable in classification. As a result, the proposed framework outperforms existing studies, offering clinicians enhanced decision-making support through its high-accuracy classification performance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging

Çetin-Kaya,

Kaya

2024

Diagnostics

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“…In tasks, it becomes necessary to prioritize sensitivity over specificity. The F1 score provides an assessment of precision which is particularly valuable when dealing with imbalanced classes [17]. As an illustration, a particular study employs Magnetic Resonance Imaging (MRI) to identify brain tumors, yielding a precision rate of 97.8% and a sensitivity rate of 97.32%.…”

Section: Overview Of Medical Imaging Techniques and The Role Of 3d Cnnsmentioning

confidence: 99%

A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging

Tiwari,

Jain,

Shetty

et al. 2023

RAiSE-2023

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Densely Connected CNN-Based XAuNet1.0 for Brain Tumor Classification via MRI Images in the IoT Era

Benyamina,

Al-Turjman

2024

Advances in Science, Technology &Amp; Innovation

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Brain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural Network

Cited by 13 publications

References 0 publications

A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging

A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging

A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging

Densely Connected CNN-Based XAuNet1.0 for Brain Tumor Classification via MRI Images in the IoT Era

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