This research proposes a Cognitive Pruning - based Spiking Neural Network (CPSNN) for brain tumor classification in Magnetic resonance imaging (MRI) images which selectively removes less significant synapses thereby reducing computation time while conserving required features.
This study proposes BrainTumor CNN, a convolutional neural network (CNN) for classifying brain tumor MRI images, which leverages transfer learning via a pre-trained ResNet-18 network, integrating data augmentation and Dropout regularization to enhance robustness and generalization.
Yi-Chen Xu· International Conference on...· 0 citations
This study aims to enhance the transparency of Convolutional Neural Network (CNN)-based brain tumor classification models by implementing Explainable Artificial Intelligence (XAI) techniques, specifically Eigen-CAM and LIME, utilizing a dataset of 3,000 MRI images.
M. A. Ghofur, Nirma Ceisa Santi, Hastie Audytra· JOURNAL OF APPLIED INFORMATI...· 0 citations
Brain tumors represent one of the most critical and life-threatening forms of cancer worldwide, and accurate automated classification of MRI scans plays a crucial role in supporting timely diagnosis and treatment planning. In this study, a deep learning-based approach for automatic brain tumor classification is propose...
Kalhara Batangala, A. Amarasinghe, U. Wijenayake· Moratuwa Engineering Researc...· 0 citations
A novel SNN model, Dynamic Gaussian-Weighted Evolving Spiking Neural Network (DyGENN) is developed and applied for the detection of Parkinson’s disease and epileptic seizure and outperforms existing models in both scenarios, suggesting its potential for broader application to other neurological conditions characterized...
Priya Das, Sarita Nanda, P. K. Sahoo et al.· Discover Computing· 0 citations
This research systematically benchmarks five CNN architectures (VGG19, DenseNet201, ResNet50, Inception-v3, and MobileNet) on balanced and naturally imbalanced MRI datasets, suggesting that VGG19 is particularly good at discriminative performance.
Tegar Anugrah Firdaus, B. Rais, Marcelinus Jonathan Salim et al.· 0 citations
This research proposes a Spatially Constrained Attention Weighted Fuzzy C-Means (SCAW-FCM)-based tumor segmentation model by integrating spatial context and attention weight to achieve more accurate boundary detection and improves tumor type classification.
Veeresh, Shiva Prakash· Recent Patents on Engineerin...· 0 citations
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