Brain tumor classification for MR images using transfer learning and fine-tuning
By Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir, Farman Ali, Zakir Ali, Saeed Ahmed, Jianfeng Lu. Comput. Medical Imaging Graph., 2019
724 citations recorded by the source corpus.
Accurate and precise brain tumor MR images classification plays important role in clinical diagnosis and decision making for patient treatment. The key challenge in MR images classification is the semantic gap between the low-level visual information captured by the MRI machine and the high-level information perceived by the human evaluator. The traditional machine learning techniques for classification focus only on low-level or high-level features, use some handcrafted features to reduce this gap and require good feature extraction and classification methods. Recent development on deep learning has shown great progress and deep convolution neural networks (CNNs) have succeeded in the images classification task. Deep learning is very powerful for feature representation that can depict low-level and high-level information completely and embed the phase of feature extraction and classification into self-learning but require large training dataset in general. For most of the medical imaging scenario, the training datasets are small, therefore, it is a challenging task to apply the deep learning and train CNN from scratch on the small dataset. Aiming this problem, we use pre-trained deep CNN model and propose a block-wise fine-tuning strategy based on transfer learning. The proposed method is evaluated on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI) benchmark dataset. Our method is more generic as it does not use any handcrafted features, requires minimal preprocessing and can achieve average accuracy of 94.82% under five-fold cross-validation. We compare our results not only with the traditional machine learning but also with deep learning methods using CNNs. Experimental results show that our proposed method outperforms state-of-the-art classification on the CE-MRI dataset.
Subfields: Computer Vision and Pattern Recognition, Neurology
View Paper: https://www.semanticscholar.org/paper/61d2e5ab597ae57d12c27613315a441d184e64af
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Domain Adaptation for Medical Image Analysis: A Survey (2021, neutral): “recognition [15, 2, 7, 10], and feature learning [14, 12, 22]”
- Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists (2020, background): “, T1, T2, T1CE, and Flair) required the determination of altered features, such as shape and texture in the MRI Image [16].”
- Accurate brain tumor detection using deep convolutional neural network (2022, methodology): “, proposed a fine-tuned VGG19 model [37].”