Optimized CNN-based Brain Tumor Segmentation and Classification using Artificial Bee Colony and Thresholding

Babu, P. Ashok; Rao, B. V. Subba; Reddy, Y. Vijay Bhaskar; Kumar, G . Rajendra; Rao, Jiahua; Koduru, Surendra Kumar Reddy; Kumar, G. Sunil

doi:10.15837/ijccc.2023.1.4577

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…There are also methods that combine traditional techniques with CNN. These approaches utilize traditional methods to adjust the learning rate of the CNN, thereby improving the classification and segmentation performance [33].…”

Section: Cnn-based Methodsmentioning

confidence: 99%

Review of deep learning-driven MRI brain tumor detection and segmentation methods

Zhang,

Luo,

Chen

et al. 2023

ACSS

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The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analyzes and compares the performance and advantages and disadvantages of different methods. Additionally, representative brain tu-mor segmentation dataset (BraTS) and its evaluation metrics are introduced, upon which the performance of various deep learning-based brain tumor segmentation methods on the BraTS 2019-2022 dataset is compared. Lastly, the challenges and future development trends in deep learning-based MRI brain tumor detection and segmentation methods are summarized and anticipated.

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Section: Cnn-based Methodsmentioning

confidence: 99%

Review of deep learning-driven MRI brain tumor detection and segmentation methods

Zhang,

Luo,

Chen

et al. 2023

ACSS

View full text Add to dashboard Cite

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“…Here, we apply an optimised CNN to the problem of identifying breast cancer in clinical samples. The input for CNN is the appropriate target class y, and the training information is a vector X of trained samples based on the backpropagation method [25]. The output of each CNN is compared to the target, and the difference between the two represents the learning error.…”

Section: Prediction Of Disease Using Deep Learning Modelmentioning

confidence: 99%

Securing the Patient’s Breast Cancer Data using Blockchain-based IBE with Deep Learning Model in IoT

D. Baby Sathiya

2024

jes

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Computational intelligence (CI) and artificial intelligence (AI) have a portion to offer in the way of making healthcare systems smart and long-lasting. These technological advances have the potential to lessen our environmental impact while simultaneously increasing expectations for performance. The use of electronic health monitoring systems in healthcare management has been crucial. E-health has the potential to offer patients useful and efficient tracking tools. However, the existing E-Health system has protection disputes. However, there are safety concerns with the present online healthcare system. Doctors with bad intentions might collude with CSPs to compromise their patients' electronic health records (EHRs) or quickly leak EHR information to other opponents for financial gain. Patients might be manipulated by clinicians who work in collusion with a Patient Healthcare Monitoring Service Provider (PHMSP). EHRs for financial gain or disclose the HER content of EHRs directly to other adversaries. Recently, block-chain has emerged as one of the most potent strategies for maintaining privacy and security. The current security issues in e-health monitoring systems are expected to be replaced by this promising security method. To prevent unauthorised parties from accessing encrypted data, blockchain uses encryption. Using a computational intelligence approach, this study presented a blockchain-based encryption framework based on identity-based encryption (IBE) to deliver secure solutions. Furthermore, the study effort utilises optimised deep learning (ODL) to forecast the patient's condition from breast cancer input data. The suggested method improves performance, with an accuracy of 0.93 during training and 0.91 during validation.

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“…The method presented by Babu et al [ 119 ] focused on categorizing and segmenting brain cancers from MRI images. Four processes compose the procedure: image denoising, segmentation of tumor, extracting features, and hybrid classification.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

Kaifi

2023

Diagnostics

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Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

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Optimized CNN-based Brain Tumor Segmentation and Classification using Artificial Bee Colony and Thresholding

Cited by 5 publications

References 32 publications

Review of deep learning-driven MRI brain tumor detection and segmentation methods

Review of deep learning-driven MRI brain tumor detection and segmentation methods

Securing the Patient’s Breast Cancer Data using Blockchain-based IBE with Deep Learning Model in IoT

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

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