The experimental results indicate that the proposed model was 99.97% accurate during training and 98.78% accurate during testing. With high accuracy and a ...
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Abstract: Brain tumors are most common in children and the elderly. It is a serious form of cancer caused by uncontrollable brain cell growth inside the ...
This study investigates the potential of deep learning, specifically the DenseNet architecture, to automate brain tumor classification.
The fine-tuned EfficientNet-B0 architecture was employed to classify four different stages of brain tumours from the MRI images.
Dec 27, 2023 · In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: ...
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Jun 30, 2024 · This study presents a novel deep learning (DL) approach utilizing the EfficientNet family for enhanced brain tumor classification and detection.
Accuracy reaches 96.94% with the EfficientNet-B0 model. The Magnetic Resonance Images (MRI) have been loaded into EfficientNet, which continuously adds hidden ...
Aug 26, 2024 · We present a model that leverages pre-trained CNNs to categorize brain cancer cases. Additionally, data augmentation techniques are employed to augment the ...
Efficient brain tumor grade classification using ensemble deep ...
pmc.ncbi.nlm.nih.gov › PMC11529038
Nov 1, 2024 · This research proposes a novel approach called Brain Tumor Grade Classification (BTGC) system which seeks to overcome the burden of classifying ...
The paper introduces a lightweight fine-tuned Convolutional Neural Network EfficientNet 'ECNN' to detect brain tumors.