Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches

Nayak, Dillip Ranjan; Padhy, Neelamadhab; Mallick, Pradeep Kumar; Bagal, Dilip Kumar; Kumar, Sachin

doi:10.3390/computers11010010

Cited by 44 publications

(12 citation statements)

References 39 publications

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“…Nayak et al 75 introduced a metaheuristics based deep learning technique for brain tumor diagnosis. The convolutional neural network (CNN) based advanced architecture called ResNetV2 is developed for brain tumor classification.…”

Section: Optimization Algorithms For Disease Identificationmentioning

confidence: 99%

“…With only 5 features out of 44, GA obtained satisfactory classification precision. The best attributes for classification are homogeneity, individual mean of difference, sum average, sum variance, and range of autocorrelation.Nayak et al75 introduced a metaheuristics based deep learning technique for brain tumor diagnosis. The convolutional neural network (CNN) based advanced architecture called ResNetV2 is developed for brain tumor classification.…”

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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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Section: Optimization Algorithms For Disease Identificationmentioning

confidence: 99%

mentioning

confidence: 99%

A review on optimization techniques for medical image analysis

Kaur

Singh²

2022

Concurrency and Computation

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“…Because it is a challenging task for these methods to accurately detect the abnormalities in the brain MRI images [ 31 ]. Moreover, a modified CNN based model was developed by [ 32 ] for the analysis of brain tumors. The authors employed CNN along with parametric optimization techniques such as the sunflower optimization algorithm (SFOA), the forensic-based investigation algorithm (FBIA), and the material generation algorithm (MGA).…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Machine Learning Approach for Brain MRI Classification

2022

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Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space’s dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.

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“…In brain tumor treatment, essential factors include its type, location, and size [1]. The intricate variations within brain tumor cells can complicate determining the tumor type and the suitable treatment strategy, potentially resulting in varying clinician assessments [4]. Therefore, in this study, a computer-aided diagnosis system was developed to classify brain tumor types accurately and quickly from MR images.…”

Section: Introductionmentioning

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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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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Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches

Cited by 44 publications

References 39 publications

A review on optimization techniques for medical image analysis

A review on optimization techniques for medical image analysis

An Enhanced Machine Learning Approach for Brain MRI Classification

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

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