Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

Kuraparthi, Swaraja; Madhavi, K. Reddy; Sujatha, C.; Valiveti, Hima Bindu; Duggineni, Chaitanya; Kollati, Meenakshi; Kora, Padmavathi; Sravan, V.

doi:10.18280/ts.380428

Cited by 41 publications

(13 citation statements)

References 20 publications

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“…Accuracy measures the model’s overall performance in correctly classifying and is calculated as the ratio of correct predictions to the total number of predictions. The mathematical expressions for recall, precision, f1-score, and accuracy are represented by Equations (10)–(13) [ 64 ].

…”

Section: Resultsmentioning

confidence: 99%

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning

Rasheed

Ullah

et al. 2023

Brain Sciences

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Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI)can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.

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…”

Section: Resultsmentioning

confidence: 99%

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning

Rasheed

Ullah

et al. 2023

Brain Sciences

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“…It is calculated by dividing the number of accurate predictions by the total number of predictions made. Equations (13)–(16) indicate the mathematical representations of precision, recall, F1-score, and accuracy [ 67 ].

…”

Section: Resultsmentioning

confidence: 99%

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

Rasheed,

Ma,

Ullah

et al. 2023

Brain Sciences

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The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses.

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“…The data set 33 used in this study was split into three separate groups: the training set, testing set, and validation set, as mentioned in Table 4. It was obtained from a publically accessible repository.…”

Section: Resultsmentioning

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

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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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Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

Cited by 41 publications

References 20 publications

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

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

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