Drug Delivery Approaches and Imaging Techniques for Brain Tumor

Bell, Mark; Rooks, Christine Pujol; Agrahari, Vibhuti

doi:10.1007/978-1-0716-1052-7_4

Cited by 2 publications

(2 citation statements)

References 57 publications

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“…The normal brain classically contains 3 types of tissues: white, gray matter and cerebrospinal fluid 7,8 . The treatment starts while determining what kind of tumors affected in the brain whether it is primary stage (Begin) or in secondary stage (Malign), and also determines the tumors size and position using MRI or the CT brain scan image 9,10 . When the tumor is found in the stem or other area, the tissue may be removed by using surgeon without harming normal brain tissue 11,12 .…”

Section: Introductionmentioning

confidence: 99%

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Dropout AlexNet‐extreme learning optimized with fast gradient descent optimization algorithm for brain tumor classification

Priya¹,

S²

2023

Concurrency and Computation

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Brain tumor is caused by the growth of abnormal cells, which forms a mass and affects the brain functions. The existing methods did not provide sufficient accuracy with high computational complexity. Therefore, in this manuscript, a Dropout AlexNet-Extreme Learning optimized with Fast Gradient Descent optimization algorithm is proposed for detecting and classifying images of brain tumor. Here, the input magnetic resonance imaging images are taken from three datasets: BRATS dataset, ISLES dataset, and RemBRANDT Dataset. Then the imageries are preprocessed to remove the noise as well as improve the superiority of the images. The image features are extracted using the Gray-Level Co-Occurrence Matrix methods. The extracted features are given to the Dropout AlexNet-XtremeLearning Machine architecture for classification. Finally, the DrpXLM classifier classifying the brain images as benign, malignant, and normal.The simulation is implemented in MATLAB. For BRATS dataset, the proposed strategy achieves34.64%, 45.36%, and 33.32% higher accuracy for benign, 37.85%, 28.94%, and 56.74% higher accuracy for malignant and 46.76%, 38.96%, and 44.86% better accuracy for normal compared with the existing methods, like Gaussian filters and long short-term memory based brain tumor detection (GF-LSTM-BTD), Shannon's-Entropy and Social-Group-Optimization based brain tumor detection (SE-SGO-BTD), Alex and Google networks with softmax layer based brain tumor detection

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Section: Introductionmentioning

confidence: 99%

“…primary stage (Begin) or in secondary stage (Malign), and also determines the tumors size and position using MRI or the CT brain scan image. 9,10 When the tumor is found in the stem or other area, the tissue may be removed by using surgeon without harming normal brain tissue. 11,12 MRI scan image is employed to the detection of brain tumor.…”

mentioning

confidence: 99%

Dropout AlexNet‐extreme learning optimized with fast gradient descent optimization algorithm for brain tumor classification

Priya¹,

S²

2023

Concurrency and Computation

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The use of RNA-based treatments in the field of cancer immunotherapy

Chehelgerdi

2023

Mol Cancer

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Over the past several decades, mRNA vaccines have evolved from a theoretical concept to a clinical reality. These vaccines offer several advantages over traditional vaccine techniques, including their high potency, rapid development, low-cost manufacturing, and safe administration. However, until recently, concerns over the instability and inefficient distribution of mRNA in vivo have limited their utility. Fortunately, recent technological advancements have mostly resolved these concerns, resulting in the development of numerous mRNA vaccination platforms for infectious diseases and various types of cancer. These platforms have shown promising outcomes in both animal models and humans. This study highlights the potential of mRNA vaccines as a promising alternative approach to conventional vaccine techniques and cancer treatment. This review article aims to provide a thorough and detailed examination of mRNA vaccines, including their mechanisms of action and potential applications in cancer immunotherapy. Additionally, the article will analyze the current state of mRNA vaccine technology and highlight future directions for the development and implementation of this promising vaccine platform as a mainstream therapeutic option. The review will also discuss potential challenges and limitations of mRNA vaccines, such as their stability and in vivo distribution, and suggest ways to overcome these issues. By providing a comprehensive overview and critical analysis of mRNA vaccines, this review aims to contribute to the advancement of this innovative approach to cancer treatment.

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Drug Delivery Approaches and Imaging Techniques for Brain Tumor

Cited by 2 publications

References 57 publications

Dropout AlexNet‐extreme learning optimized with fast gradient descent optimization algorithm for brain tumor classification

Dropout AlexNet‐extreme learning optimized with fast gradient descent optimization algorithm for brain tumor classification

The use of RNA-based treatments in the field of cancer immunotherapy

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