Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

Sharma, Sarang; Gupta, Sheifali; Gupta, Deepali; Juneja, Abhinav; Khatter, Harsh; Malik, Sapna; Bitsue, Zelalem Kiros

doi:10.1155/2022/3065656

Cited by 25 publications

(15 citation statements)

References 25 publications

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“…These treatment options are based on the type of tumor and its stage during detection time. The traditional identification methods use simple ML-oriented algorithms that only extract a few features [ 56 ]. This study introduces a unique feature ensemble-based method for precisely classifying brain cancers from MR images.…”

Section: Resultsmentioning

confidence: 99%

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A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

Kibriya

Amin²,

Kim

et al. 2023

Sensors

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One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient’s health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors. These approaches, however, result in poor performance; hence, there is a need for an efficient technique to perform precise diagnoses. This paper suggests a novel approach for brain tumor detection via an ensemble of deep and hand-crafted feature vectors (FV). The novel FV is an ensemble of hand-crafted features based on the GLCM (gray level co-occurrence matrix) and in-depth features based on VGG16. The novel FV contains robust features compared to independent vectors, which improve the suggested method’s discriminating capabilities. The proposed FV is then classified using SVM or support vector machines and the k-nearest neighbor classifier (KNN). The framework achieved the highest accuracy of 99% on the ensemble FV. The results indicate the reliability and efficacy of the proposed methodology; hence, radiologists can use it to detect brain tumors through MRI (magnetic resonance imaging). The results show the robustness of the proposed method and can be deployed in the real environment to detect brain tumors from MRI images accurately. In addition, the performance of our model was validated via cross-tabulated data.

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Section: Resultsmentioning

confidence: 99%

“…However, the system is computationally expensive. Sharma et al [ 56 ] used the concept of transfer learning for brain tumor detection. They employed VGG-16 architecture that attained 94.&% accuracy on the Kaggle database.…”

Section: Resultsmentioning

confidence: 99%

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

Kibriya

Amin²,

Kim

et al. 2023

Sensors

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“…Validation of the proposed model against public datasets is pivotal, assessing its accuracy, sensitivity, specificity, and positive predictive value. This multifaceted approach positions the research at the forefront of advancing brain tumor classification methodologies [15]- [18].…”

Section: Establishing Context and Significancementioning

confidence: 99%

NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning

Arora,

Mishra

2024

Preprint

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This study presents a robust framework for the classification of brain tumors, beginning with meticulous data curation from 233 patients. The dataset comprises a diverse range of T1-weighted contrast-enhanced images, encompassing meningioma, glioma, and pituitary tumor types. Rigorous organization, pre-processing, and augmentation techniques are applied to optimize model training. The proposed self-adaptive model incorporates a cutting-edge algorithm, leveraging Adaptive Contrast Limited Histogram Equalization (CLAHE) and Self-Adaptive Spatial Attention. CLAHE enhances grayscale images by tailoring contrast to the unique characteristics of each region. The Self-Adaptive Spatial Attention, implemented through an Attention Layer, dynamically assigns weights to spatial locations, thereby enhancing sensitivity to critical brain regions. The model architecture integrates transfer learning models, including DenseNet169, DenseNet201, ResNet152, and InceptionResNetV2, contributing to its robustness. DenseNet169 serves as a feature extractor, capturing hierarchical features through pre-trained weights. Model adaptability is further enriched by components such as batch normalization, dropout, layer normalization, and an adaptive learning rate strategy, mitigating overfitting and dynamically adjusting learning rates during training. Technical details, including the use of the Adam optimizer and softmax activation function, underscore the model's optimization and multi-class classification capabilities. The proposed model, which amalgamates transfer learning and adaptive mechanisms, emerges as a powerful tool for brain tumor detection and classification in medical imaging. Its nuanced comprehension of brain tumor images, facilitated by self-adaptive attention mechanisms, positions it as a promising advancement in computer-aided diagnosis in neuroimaging. Leveraging DenseNet201 with a self-adaptive mechanism, the model surpasses previous methods, achieving an accuracy of 94.85%, precision of 95.16%, and recall of 94.60%, showcasing its potential for enhanced accuracy and generalization in the challenging realm of medical image analysis.

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“…A tumor is created when aberrant cells divide out of control, generating a mass that might impair the tissue or organ's ability to function normally [1,2]. The genesis, foundation, and cell types of tumors are distinct characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…The genesis, foundation, and cell types of tumors are distinct characteristics. The brain shows early stages of tumors mostly in the cerebrum region, although secondary tumors enter the brain from other parts of the body [2]. Cancerous tumors are classified as malignant (high-grade) or benign (low-grade), respectively.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation Using Deep Learning on MRI Images

2023

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Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model’s output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%.

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Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

Cited by 25 publications

References 25 publications

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning

Brain Tumor Segmentation Using Deep Learning on MRI Images

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