Automated Brain Tumor Segmentation and Classification Through MRI Images

Gull, Sahar; Akbar, Shahzad; Hassan, Syed Ale; Rehman, Amjad; Sadad, Tariq

doi:10.1007/978-3-030-97255-4_13

Cited by 11 publications

(1 citation statement)

References 36 publications

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“…27 In the medical field, artificial intelligence [28][29][30] is explored in a variety of ways. Deep learning helps in many areas of research for the automated detection of different diseases such as hypertensive retinopathy, 31,32 brain tumor, [33][34][35][36] papilledema, 37,38 glaucoma, [39][40][41] melanoma, 42,43 alzheimer disease, [44][45][46][47] central serous retinopathy, 48 leukemia, 49,50 and liver cancer 51 in healthcare. Among DL techniques, the neural network has acquired a lot of popularity and is widely used in image classification.…”

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confidence: 99%

A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images

Gull

Akbar

Naqi

2023

Int J Imaging Syst Tech

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Brain tumor is the 10th major cause of death among humans. The detection of brain tumor is a significant process in the medical field. Therefore, the objective of this research work is to propose a fully automated deep learning framework for multistage classification. Besides, this study focuses on to develop an efficient and reliable system using a convolutional neural network (CNN). In this study, the fast bounding box technique is used for segmentation. Moreover, the CNN layers‐based three models are developed for multistage classification through magnetic resonance images on three publicly available datasets. The first dataset is obtained from Kaggle Repository (Dataset‐1), the second dataset is known as Figshare (Dataset‐2), and the third dataset is called REMBRANDT (Dataset‐3) to classify the MR images into different grades. Different augmentation techniques are applied to increase the data size of MR images. In pre‐processing, the proposed models achieved higher Peak Signal‐to‐Noise ratio to remove noise. The first proposed deep CNN framework mentioned as Classification‐1 has obtained 99.40% accuracy, which classified MR images into two classes, that is (i) normal and (ii) abnormal, while the second proposed CNN framework mentioned as Classification‐2 has obtained 97.78% accuracy, which classified brain tumor into three types, which are meningioma, glioma, and pituitary. Similarly, the third developed CNN framework mentioned as Classification‐3 has obtained 98.91% accuracy that further classified MR images of tumors into four different classes as: Grade I, Grade II, Grade III, and Grade IV. The results demonstrate that the proposed models achieved better performance on three large and diverse datasets. The comparison of obtained outcomes shows that the developed models are more efficient and effective than state‐of‐the‐art methods.

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confidence: 99%

A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images

Gull

Akbar

Naqi

2023

Int J Imaging Syst Tech

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Detection of brain space-occupying lesions using quantum machine learning

Amin

Anjum

Gul

et al. 2023

Neural Comput & Applic

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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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Automated Brain Tumor Segmentation and Classification Through MRI Images

Cited by 11 publications

References 36 publications

A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images

A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images

Detection of brain space-occupying lesions using quantum machine learning

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

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