Brain-DeepNet: A Deep Learning Based Classifier for Brain Tumor Detection and Classification

Habiba, Sultana Umme; Islam, Md. Khairul; Nahar, Lutfun; Tasnim, Farzana; Hossain, Mohammad Shahadat; Andersson, Karl

doi:10.1007/978-3-031-19958-5_52

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…Similarly, S. U. Habiba et al proposed in [27] a transfer learning approach using the InceptionV3 and DenseNet201 models to categorize brain tumors on a publicly accessible dataset. Data augmentation strategies can be employed to obtain accurate classification results by enhancing the dataset and mitigating overfitting.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Brain Tumor Identification in Biomedical Radiology Images: A Multi-Model Ensemble Deep Learning Approach

Natha,

Laila,

Gashim

et al. 2024

Applied Sciences

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Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person’s life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient’s survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called “SETL_BMRI”, which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model’s effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Brain Tumor Identification in Biomedical Radiology Images: A Multi-Model Ensemble Deep Learning Approach

Natha,

Laila,

Gashim

et al. 2024

Applied Sciences

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“…The results show that the maximum classification accuracy of 95.3% was achieved on SVM. Habiba et al [ 37 ] presented a study on the detection and classification of brain tumors using deep learning-based classifiers that extract features from MRI images. The researchers used a publicly available dataset and a transfer learning approach with the InceptionV3 and DenseNet201 models.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans

2023

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Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers.

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“…Depending on the part of the brain damaged, a stroke can result in death, a coma, paralysis, impaired speech, memory loss, and difficulty reasoning. The fifth most common cause of mortality and the leading cause of disability in the United States is stroke (102). Twenty-nine billion dollars are anticipated to be spent on direct medical expenses each year, and 795,000 Americans have a new or recurrent stroke each year [29].…”

Section: E) Strokementioning

confidence: 99%

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

jain

2023

Preprint

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BrainWave Diagnostics, an emerging field, leverages electroencephalography (EEG) data for cost-effective and resource-efficient neurological disorder detection. Although EEGs are commonly used for neurological disease detection, their low signal intensity and nonlinear features pose analytical challenges. This review explores the use of high-performance computational tools, machine learning, and deep learning methods in diagnosing a range of neurological disorders, including epilepsy, Parkinson's disease, autism, ADHD, stroke, tumors, schizophrenia, Alzheimer's, depression, and alcohol use disorder. The increasing prevalence of neurological disorders and their resource burden underscores the urgency of these diagnostic advancements. Future research can consider multi-modal approaches, providing practical solutions for neurological disorder detection beyond EEGs, with potential applications in diverse signal analysis domains.

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Brain-DeepNet: A Deep Learning Based Classifier for Brain Tumor Detection and Classification

Cited by 12 publications

References 28 publications

Automated Brain Tumor Identification in Biomedical Radiology Images: A Multi-Model Ensemble Deep Learning Approach

Automated Brain Tumor Identification in Biomedical Radiology Images: A Multi-Model Ensemble Deep Learning Approach

Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

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