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Kalifa Shantta,Otman Basir
Pág. 55 - 61
Even with the enormous progress in medical technology, brain tumor detection is still an extremely tedious and complex task for the physicians. The early and accurate detection of brain tumors enables effective and efficient therapy and thus can result i...
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Sarfaraz Natha, Umme Laila, Ibrahim Ahmed Gashim, Khalid Mahboob, Muhammad Noman Saeed and Khaled Mohammed Noaman
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 appropri...
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Dimitrios Kollias, Karanjot Vendal, Priyankaben Gadhavi and Solomon Russom
Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms. The accurate determination of the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is crucial for personalized t...
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Md Ishtyaq Mahmud, Muntasir Mamun and Ahmed Abdelgawad
Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activiti...
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Rahmeh Ibrahim, Rawan Ghnemat and Qasem Abu Al-Haija
Convolutional Neural Networks (CNNs) have exhibited remarkable potential in effectively tackling the intricate task of classifying MRI images, specifically in Alzheimer?s disease detection and brain tumor identification. While CNNs optimize their paramet...
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Ayesha Younis, Li Qiang, Charles Okanda Nyatega, Mohammed Jajere Adamu and Halima Bello Kawuwa
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tum...
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Manar Ahmed Hamza, Hanan Abdullah Mengash, Saud S. Alotaibi, Siwar Ben Haj Hassine, Ayman Yafoz, Fahd Althukair, Mahmoud Othman and Radwa Marzouk
A brain tumor (BT) is an abnormal development of brain cells that causes damage to the nerves and blood vessels. An accurate and early diagnosis of BT is important to prevent future complications. Precise segmentation of the BT provides a basis for surgi...
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Inayatul Haq, Najib Ullah, Tehsen Mazhar, Muhammad Amir Malik and Iqra Bano
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Anuja Arora, Ambikesh Jayal, Mayank Gupta, Prakhar Mittal and Suresh Chandra Satapathy
Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed informat...
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Duc-Ky Ngo, Minh-Trieu Tran, Soo-Hyung Kim, Hyung-Jeong Yang and Guee-Sang Lee
Segmenting brain tumors accurately and reliably is an essential part of cancer diagnosis and treatment planning. Brain tumor segmentation of glioma patients is a challenging task because of the wide variety of tumor sizes, shapes, positions, scanning mod...
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Alessandra Maria Vitale, Radha Santonocito, Giuseppe Vergilio, Antonella Marino Gammazza, Claudia Campanella, Everly Conway de Macario, Fabio Bucchieri, Alberto J. L. Macario and Celeste Caruso Bavisotto
Primary and metastatic brain tumors are usually serious conditions with poor prognosis, which reveal the urgent need of developing rapid diagnostic tools and efficacious treatments. To achieve these objectives, progress must be made in the understanding ...
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Hapsari Peni Agustin Tjahyaningtijas, Andi K Nugroho, Cucun Very Angkoso, I Ketut Edy Purnama, Mauridhi Hery Purnomo
Pág. 161 - 177
Glioblastoma is listed as a malignant brain tumor. Due to its heterogeneous composition in one area of the tumor, the area of tumor is difficult to segment from healthy tissue. On the other side, the segmentation of brain tumor MRI imaging is also ...
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Maxwell Uhlich, Russell Greiner, Bret Hoehn, Melissa Woghiren, Idanis Diaz, Tatiana Ivanova and Albert Murtha
Automated brain tumor segmenters typically run a ?skull-stripping? pre-process to extract the brain from the 3D image, before segmenting the area of interest within the extracted volume. We demonstrate that an effective existing segmenter can be improved...
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Maxwell Uhlich, Russell Greiner, Bret Hoehn, Melissa Woghiren, Idanis Diaz, Tatiana Ivanova and Albert Murtha
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Norah Fahd Alhussainan, Belgacem Ben Youssef and Mohamed Maher Ben Ismail
Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance images (MRIs), a process that is prone to human error and is also time consuming. Recent advancements leverage machine learning models to categorize tumors, such a...
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Wandile Nhlapho, Marcellin Atemkeng, Yusuf Brima and Jean-Claude Ndogmo
The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (M...
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Navid Khalili Dizaji and Mustafa Dogan
Brain tumors are one of the deadliest types of cancer. Rapid and accurate identification of brain tumors, followed by appropriate surgical intervention or chemotherapy, increases the probability of survival. Accurate determination of brain tumors in MRI ...
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Szidónia Lefkovits, László Lefkovits and László Szilágyi
Automatic brain tumor segmentation from multimodal MRI plays a significant role in assisting the diagnosis, treatment, and surgery of glioblastoma and lower glade glioma. In this article, we propose applying several deep learning techniques implemented i...
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Nikhil Gala,K.D. Desai
Pág. 279 - 285
Magnetic resonance imaging (MRI) system projects the anatomical structures of brain ontospatial domain. Three-dimensional (3D) construction of brain tumor using several slides of MRI hasalways been of keen interest for diagnosis and for research purpose....
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In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human b...
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