Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data

Tandel, Gopal S.; Tiwari, Ashish; Kakde, O. G.; Gupta, Neha; Saba, Luca; Suri, Jasjit S.

doi:10.3390/diagnostics13030481

Cited by 41 publications

(17 citation statements)

References 121 publications

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“…The authors also studied three different vision transformers, but they did not obtain satisfactory results. Another work [46] proposed an ensemble method based on five CNNs using various datasets.…”

Section: Related Workmentioning

confidence: 99%

Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging

Reyes,

Sánchez

2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging

Reyes,

Sánchez

2024

Heliyon

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“…Data augmentation, such as random rotations, random scaling, random elastic deformations, gamma correction, and mirroring, was performed to avoid overfitting. Resnet18 (32) was used as the base architecture. The loss function was the combination of focal loss (33) and cross entropy in Eq.…”

Section: D Deep Learning Classification Networkmentioning

confidence: 99%

The potential for different computed tomography-based machine learning networks to automatically segment and differentiate pelvic and sacral osteosarcoma from Ewing’s sarcoma

Yin¹,

Wang²,

Wang³

et al. 2023

Quant Imaging Med Surg

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Background: This study aimed to explore optimal computed tomography (CT)-based machine learning and deep learning methods for the identification of pelvic and sacral osteosarcomas (OS) and Ewing's sarcomas (ES).Methods: A total of 185 patients with pathologically confirmed pelvic and sacral OS and ES were analyzed.We first compared the performance of 9 radiomics-based machine learning models, 1 radiomics-based convolutional neural networks (CNNs) model, and 1 3-dimensional (3D) CNN model, respectively. We then proposed a 2-step no-new-Net (nnU-Net) model for the automatic segmentation and identification of OS and ES. The diagnoses by 3 radiologists were also obtained. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate the different models.Results: Age, tumor size, and tumor location showed significant differences between OS and ES (P<0.01).For the radiomics-based machine learning models, logistic regression (LR; AUC =0.716, ACC =0.660) performed best in the validation set. However, the radiomics-based CNN model had an AUC of 0.812 and ACC of 0.774 in the validation set, which were higher than those of the 3D CNN model (AUC =0.709, ACC =0.717). Among all the models, the nnU-Net model performed best, with an AUC of 0.835 and an ACC of 0.830 in the validation set, which was significantly higher than the primary physician's diagnosis (ACCs ranged from 0.757 to 0.811) (P<0.01). Conclusions:The proposed nnU-Net model could be an end-to-end, non-invasive, and accurate auxiliary diagnostic tool for the differentiation of pelvic and sacral OS and ES.

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“…Machine learning has been used to differentiate among various tumor types using MRI data. Convolutional Neural Networks (CNN) have also been used to classify brain tumors using deep learning on MRI data 22 , 23 .…”

Section: Introductionmentioning

confidence: 99%

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

Ullah,

Khan,

Almujally

et al. 2024

Sci Rep

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A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii) an expert in the field who can assess the output of deep learning models. These difficulties motivate us to propose an efficient and accurate system based on deep learning and evolutionary optimization for the classification of four types of brain modalities (t1 tumor, t1ce tumor, t2 tumor, and flair tumor) on a large-scale MRI database. Thus, a CNN architecture is modified based on domain knowledge and connected with an evolutionary optimization algorithm to select hyperparameters. In parallel, a Stack Encoder–Decoder network is designed with ten convolutional layers. The features of both models are extracted and optimized using an improved version of Grey Wolf with updated criteria of the Jaya algorithm. The improved version speeds up the learning process and improves the accuracy. Finally, the selected features are fused using a novel parallel pooling approach that is classified using machine learning and neural networks. Two datasets, BraTS2020 and BraTS2021, have been employed for the experimental tasks and obtained an improved average accuracy of 98% and a maximum single-classifier accuracy of 99%. Comparison is also conducted with several classifiers, techniques, and neural nets; the proposed method achieved improved performance.

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Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data

Cited by 41 publications

References 121 publications

Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging

Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging

The potential for different computed tomography-based machine learning networks to automatically segment and differentiate pelvic and sacral osteosarcoma from Ewing’s sarcoma

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

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