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Navaneethakrishna Makaram, Sarvagya Gupta, Matthew Pesce, Jeffrey Bolton, Scellig Stone, Daniel Haehn, Marc Pomplun, Christos Papadelis, Phillip Pearl, Alexander Rotenberg, Patricia Ellen Grant and Eleonora Tamilia
In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternati...
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Raul-Ronald Galea, Laura Diosan, Anca Andreica, Loredana Popa, Simona Manole and Zoltán Bálint
Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods...
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Krzysztof Malczewski
One of the most challenging aspects of medical modalities such as Computed Tomography (CT) as well hybrid techniques such as CT/PET (Computed Tomography/Positron emission tomography) and PET/MRI is finding a balance between examination time, radiation do...
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Meletios Liaskos, Michalis A. Savelonas, Pantelis A. Asvestas, Marios G. Lykissas and George K. Matsopoulos
Intervertebral disc (IVD) localization and segmentation have triggered intensive research efforts in the medical image analysis community, since IVD abnormalities are strong indicators of various spinal cord-related pathologies. Despite the intensive res...
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Bijen Khagi, Kun Ho Lee, Kyu Yeong Choi, Jang Jae Lee, Goo-Rak Kwon and Hee-Deok Yang
This paper presents an efficient computer-aided diagnosis (CAD) approach for the automatic detection of Alzheimer?s disease in patients? T1 MRI scans using the voxel-based morphometry (VBM) analysis of the region of interest (ROI) in the brain. The idea ...
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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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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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Youssef Skandarani, Pierre-Marc Jodoin and Alain Lalande
Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize...
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Sumit Salunkhe, Mrinal Bachute, Shilpa Gite, Nishad Vyas, Saanil Khanna, Keta Modi, Chinmay Katpatal and Ketan Kotecha
Alzheimer?s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already be...
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