Meta-heuristic-based FCM-UNet segmentation with multi-objective function and deep learning for brain tumour classification

Kumar, Ashiwani; Kumar, Preetam

doi:10.1080/21681163.2022.2092034

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Using an MRI, researchers suggested automated device could determine if a tumor was benign or malignant. Adam Salp Water Wave Optimization using a Deep Convolutional Neural Networks (AdamSWO-DCNN) and Adam Sewing Training centered Optimization using the UNet++ (AdamSTBO+UNet++) are two examples of the novel BT classification and segmentation techniques developed in [35]. In this case, AdamSTBO is created by combining the Adam optimiser's idea with the improvement function of the Sewing Training Based Optimization (STBO) algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Attention based MobileNetv2 Model for Brain Tumor Segmentation and Severity of Tumor Classification using Support Vector Machine

Pavithra,

Nidhya,

Vinothini

et al. 2023

Preprint

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Brain tumors are lumps of aberrant tissue that can develop into cancer and have a significant negative influence on a person's health. MRI scans of the brain can reveal them. Segmentation and classification are two elements in these approaches that are extremely crucial. As opposed to anatomical organ segmentation, tumor segmentation is much more difficult due to the variety in size, location, and shape of tumors. For this reason, it is imperative to build reliable, precise, and effective deep learning-based methods. Recent deep learning techniques for classifying and segmenting brain tumors produced encouraging results. These approaches, however, have heavy-weight architectures by nature, necessitating more storage and costly training procedures because of the enormous number of training parameters they must be fed. It is crucial to investigate transportable deep learning models without compromising classification precision. In this research, we provide compact deep neural network models using the pre-trained Attentiveness MobileNetV2 models along with the attention module. The four phases of the proposed system are preliminary processing, division, extracting and categorizing features, and severity classification. Anisotropic diffusion processing as well as data enhancement methods are used initially. The tumor region is then segmented using the proposed modified dimensional U-Net (3D-M-U-Net). Finally, the extraction and classification of features are implemented using the Compact MobileNetV2 framework. Here, the high-level tumor-based information is initially recovered from the convolution features. The important semantic information is then captured using an attention module. Once high-level tumor-based data as well as fascinating semantic information have been combined in the convolutional and focused modules, fully linked layers as well as the layer of softmax are utilized to categorize tumours into either benign or dangerous. Finally, Support Vector Machine (SVM) is used to categorize tumors into moderate, severe, and mild phases. The suggested approach was tested on the high-quality brain cancer images available in the Brats-2020 as well as Brats-2019 datasets. In regards to precision, recall, accuracy, F-Score, Dice Similarities Coefficient (DSC), as well as Structural Similarity Indicator Matrix (SSIM), the suggested model outperforms existing traditional and hybrid models. It was also the most effective and productive method tested. The suggested model has a 99.9% accuracy, a 99.9% precision, and a 99.8% recall across both datasets.

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Section: Related Workmentioning

confidence: 99%

A Lightweight Attention based MobileNetv2 Model for Brain Tumor Segmentation and Severity of Tumor Classification using Support Vector Machine

Pavithra,

Nidhya,

Vinothini

et al. 2023

Preprint

View full text Add to dashboard Cite

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Assertion of Low Bone Mass in Osteoporotic X-ray images using Deep Learning Technique

Amiya

Ramaraj

Murugan

et al. 2022

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)

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Improved Brain Tumor Segmentation in MR Images with a Modified U-Net

Alquran,

Alslatie,

Rababah

et al. 2024

Applied Sciences

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Detecting brain tumors is crucial in medical diagnostics due to the serious health risks these abnormalities present to patients. Deep learning approaches can significantly improve localization in various medical issues, particularly brain tumors. This paper emphasizes the use of deep learning models to segment brain tumors using a large dataset. The study involves comparing modifications to U-Net structures, including kernel size, number of channels, dropout ratio, and changing the activation function from ReLU to Leaky ReLU. Optimizing these parameters has notably enhanced brain tumor segmentation in MR images, achieving a Global Accuracy of 99.4% and a dice similarity coefficient of 90.2%. The model was trained, validated, and tested on many magnetic resonance images, with a training time not exceeding 19 min on a powerful GPU. This approach can be extended in medical care and hospitals to assist radiologists in identifying tumor locations and suspicious regions, thereby improving diagnosis and treatment effectiveness. The software could also be integrated into MR equipment protocols.

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Meta-heuristic-based FCM-UNet segmentation with multi-objective function and deep learning for brain tumour classification

Cited by 3 publications

References 39 publications

A Lightweight Attention based MobileNetv2 Model for Brain Tumor Segmentation and Severity of Tumor Classification using Support Vector Machine

A Lightweight Attention based MobileNetv2 Model for Brain Tumor Segmentation and Severity of Tumor Classification using Support Vector Machine

Assertion of Low Bone Mass in Osteoporotic X-ray images using Deep Learning Technique

Improved Brain Tumor Segmentation in MR Images with a Modified U-Net

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