A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Amin, Javeria; Anjum, Muhammad Almas; Sharif, Muhammad; Jabeen, Saima; Kadry, Seifedine; Moreno-Ger, Pablo

doi:10.1155/2022/3236305

Cited by 58 publications

(20 citation statements)

References 72 publications

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“…Technologies built on ML have the potential to increase the accuracy and efficacy of identifying brain tumors, perhaps resulting in more effective treatment and better patient outcomes. 16,17 In medical practice and for increasing survival rates, the early diagnosis of brain tumors is of the highest importance. Different treatment modalities are required for brain tumors since they come in a broad variety of sizes, forms, and traits.…”

Section: Related Workmentioning

confidence: 99%

“…The research investigates the effects of multiclass classification of brain tumors by employing a pre-trained ResNet50 model and CNN architecture. 17 AUGMENTATION A model's training and the framework given to its learning system have a significant impact on how successful the model is. The inability of the chosen data set to match the precise data annotation criteria is a frequent problem.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection

Raza,

Alshehri,

Almakdi

et al. 2023

Int J Imaging Syst Tech

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Brain tumors pose a serious neurological threat to human life, necessitating improved detection and classification methods. Deep transfer learning (TL), in particular in key tumor categories such as meningioma, pituitary, glioma, and instances without tumors, has shown to be a new and successful method for tumor identification and classification. In this work, the efficacy of two pre‐trained TL methods—Inceptionv3 and DenseNet121—was examined for correctly classifying certain kinds of brain tumors. The experimental findings show that the DenseNet‐121 model, using the TL approach, performed better than other models in terms of accuracy for the identification and classification of brain tumors. The classification test results were impressive, with DenseNet‐121 reaching an astounding 99.95% accuracy and precision, recall, and F1‐measure scores of 97.7%, 92.1%, and 94.8%, respectively. DenseNet‐121 demonstrated 100% and 92.42% training and validation accuracies, respectively, highlighting its potential as an effective and precise diagnosis tool for brain malignancies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection

Raza,

Alshehri,

Almakdi

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Features are extracted from pre-trained VGG-16 and input to SVM for discrimination between infected/uninfected cells of malaria with 93.1% accuracy (22). Custom CNN (23)(24)(25)(26)(27)(28)(29)(30)(31) and pretrained efficientnet-b0 model are used for features extraction and they provided accuracy of 97.74 and 98.82%, respectively (20). DCNN model is used for the classification of blood smear images with a 94.79% classification accuracy (32).…”

Section: Related Workmentioning

confidence: 99%

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Amin

Sharif

Mallah

et al. 2022

Front. Public Health

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Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.

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“…Because we aimed for an accurate and reliable tumor detection method, we used three CNN architectures, a deep architecture Inception-V3 and the others being shallow architectures VGG-16 (20) and VGG-19 (21) .…”

Section: Architecturesmentioning

confidence: 99%

Brain Tumor Detection Using Transfer Learning in Deep Learning

Alla¹,

Athota²

2022

IJST

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Background/Objectives: Magnetic resonance imaging (MRI) is widely used for tumor evaluation. However, MRI generates enormous data, making manual segmentation difficult in a reasonable amount of time, which limits the use of accurate measurements in clinical practice. Therefore, this study focuses on the automatic and reliable segmentation methods which are needed for early diagnosis of brain tumors. Methods: In this study, we used deep learning-based convolutional neural networks (CNNs) to extract features and automatically classify brain tumors based on MRI images. In addition to conventional CNNs, the application of transfer learning was investigated by using three types of CNNs (Inception-V3, VGG-16, and VGG-19) to achieve reasonable accuracy, with fine tuning of the final layers to improve the accuracy of the models. Findings: The results show that applying transfer learning to a CNN achieves high accuracy in less time and with a smaller dataset. VGG-19 achieves 97 % accuracy, and VGG-16 achieves 96 % accuracy, which is better than the accuracy of Inception-V3 (89 %). Our proposed model using CNNs with transfer learning provides more robust automatic and reliable segmentation methods. Novelty: The novelty of this work is the use of Transfer Learning in conjunction with deep learning-based CNNs, as Transfer Learning provides a novel technique to analyse data with few annotations by transferring information from the source domain to the target domain.

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A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Cited by 58 publications

References 72 publications

Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection

Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Brain Tumor Detection Using Transfer Learning in Deep Learning

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