Detection and Automated Classification of Brain Tumor Types in MRI Images using Convolutional Neural Network with Grid Search Optimization

Devi, Renuka; Samundeeswari, Vijaya

doi:10.1109/i-smac52330.2021.9640670

Cited by 5 publications

(2 citation statements)

References 14 publications

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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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A Siamese network optimization using genetic algorithm for brain diseases

Saif,

Ghnemat,

Abu Al‐Haija

2024

IET Image Processing

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The complex nature of human brain tissues is important in ensuring accurate diagnosis to save human lives. Research on early detection of brain diseases has gained significant prominence within medical intelligence using highly complex model architectures with only a single label that cannot be verified. This paper introduces an innovative approach to Siamese Network Genetic Algorithm (SN‐GA) leveraging Siamese contrastive learning for classifying brain images across diverse diseases. Our core architecture is a Bi‐Convolutional Neural Network (Bi‐CNN) optimized by a genetic algorithm to enhance brain image classification. Specifically, five widely recognized transfer learning‐based architectures, namely AlexNet, Efficient‐B0, VGG‐16, ResNet‐50, and Inception‐v3, have been incorporated to evaluate the effectiveness of the proposed SN‐GA system. The performance of these models has been rigorously analyzed and compared using two distinct datasets: Brain tumors and Alzheimer's datasets. The experimental results robustly affirm the efficacy of the proposed Siamese model, yielding exceptional levels of accuracy, precision, and recall, all peaking at 97%. These findings underscore the potential and resilience of the optimized Siamese network in the context of brain disease classification, emphasizing its significance in advancing the field of medical imaging and diagnosis, with implications for early intervention and patient care.

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