Deep Learning Based Brain Tumor Segmentation: Recent Updates

Patil, R. R.; Ansingkar, Nirupama; Deshmukh, Prapti D.

doi:10.1007/978-981-19-1122-4_42

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…The challenge is greatest in regions with anomalous colour and shape, which radiologists must identify for scientific study. Image analysis techniques necessary for detecting and diagnosing brain tumours can benefit from the application of Deep learning technologies [7]. In view of the present knowledge state and the high need for Deep Learning [6][7][8][9][10], this study proposes data training to deal with the complexity involved in recognizing brain tumours while proposing solutions.…”

Section: The Requirement For Detectionmentioning

confidence: 99%

Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner

Agrawal¹

2023

ITM Web Conf.

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Brain cancer is a potentially fatal illness that affects the brain. To preserve lives, early tumour detection is now crucial. Imaging in medicine is one method for diagnosing brain tumours. To help find brain tumours, researchers are turning to deep learning. Error in individual early diagnosis of the condition has been demonstrated to be reduced using deep learning algorithms. In the case of brain tumours, even a slight misdiagnosis might have serious consequences. When it comes to processing medical images, spotting brain tumours is still a difficult task. It’s difficult to spot the tumour on a brain scan. The precision of the image is impacted by many disturbances and a delay. We used deep learning methods to describe brain disorders in our paper. Brain disease detection utilizing deep learning methods is related to the study of new information. Proposed TL-based DenseNet121 model achieved accuracy, sensitivity, specificity, F1Score, precision, and IoU of 98.38, 97.33, 99.1, 98.23, 98.62, and 96.62 respectively. The results obtained on the brain tumor data set demonstrate that the proposed method outperforms others in terms of F1-score, Precision, Sensitivity, Accuracy, Specificity, and IoU.

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Section: The Requirement For Detectionmentioning

confidence: 99%

Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner

Agrawal¹

2023

ITM Web Conf.

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“…These approaches usually rely on picture content analysis, which is a key component of a wide variety of computer vision applications and is continuously undergoing technological advancement. Consequently, the advancement in deep learning and Internet of Things (IoT) in health sector are today considered to be the primary driving forces behind the development of health industry [10,11]. It has been noticed that the healthcare business is showing a growing interest in the identification of illnesses, with a special emphasis on the enhancement of the implementation of E-Health services [12].…”

Section: Introductionmentioning

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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A systematic review on brain tumor detection using deep learning

Jain,

Sachdeva

2024

1ST INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENTS IN COMPUTING TECHNOLOGIES &Amp; ENGINEERING

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Deep Learning Based Brain Tumor Segmentation: Recent Updates

Cited by 7 publications

References 23 publications

Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner

Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner

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

A systematic review on brain tumor detection using deep learning

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