An automatic MRI brain image segmentation technique using edge–region-based level set

Aghazadeh, Nasser; Moradi, Paria; Castellano, Giovanna; Noras, Parisa

doi:10.1007/s11227-022-04948-9

Cited by 5 publications

(1 citation statement)

References 42 publications

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“…If the new data set is small, but similar to ImageNet, then the classical method of transferring training should be applied. In this case, also network setup will be considered as classifier training [12]. If the data set is small and very different from the ImageNet image set, then the best strategy for configuring the model is to train it with sequential unfreezing of the convolutional layers of the pre-trained model, starting from the last one, until an acceptable classification accuracy is achieved on the used set of images.…”

Section: Transfer Learning Methods In Image Recognitionmentioning

confidence: 99%

Deep Learning Transfer Methods for Biomedical Classification of Images

Al-Janabi

2024

Ijirme

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Research using computers has been carried out on the effectiveness of applying deep learning transfer methods to solve the problem of identifying human brain tumors using MRI imaging. Various deep learning and fine-tuning methodologies of models have been proposed and implemented. The deep convolution networks MobileNetV2, VGG-16, Xception and ResNet-50, trained on the ImageNet image set, were used as basic models. A deep convolutional neural network 2D-CNN has also been developed and trained. A computer study of the performance indicators revealed that the fine-tuning method was effective On an enlarged data set, the Xception model outperformed other deep learning models in terms of accuracy: the clarity with which brain tumors are classified using MRI images was 94%, precision 97.7%, recall 94.01%, f1 score 96%, AUC 96.90%.

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Section: Transfer Learning Methods In Image Recognitionmentioning

confidence: 99%

Deep Learning Transfer Methods for Biomedical Classification of Images

Al-Janabi

2024

Ijirme

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Unveiling the Best Edge Detection Algorithm for Brain Magnetic Resonance Imaging: A Qualitative and Quantitative Comparative Study

Verma,

Srivastava,

Mishra

2024

Lecture Notes in Electrical Engineering

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Nature inspired optimization algorithms for medical image segmentation: a comprehensive review

Houssein,

Mohamed,

Djenouri

et al. 2024

Cluster Comput

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Image segmentation is the process of splitting a digital image into distinct segments or categories based on shared characteristics like texture, color, and intensity. Its primary aim is to simplify the image for easier analysis while preserving its important features. Each pixel in the image is assigned a label, grouped together by pixels with similar traits together. Segmentation helps to delineate boundaries and identify objects such as curves or lines within the image. The process generates a series of segmented images that cover the entire original image. This article reviews emerging applications of image segmentation in medical diagnostics, specifically employing nature-inspired optimization algorithms (NIOAs). It begins by outlining different segmentation methods and NIOAs types, then by examining relevant databases and medical imaging technologies. The study draws on a diverse range of research sources. Finally, this paper briefly discusses the challenges and future trends of medical image segmentation using NIOAs to detect different diseases.

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An automatic MRI brain image segmentation technique using edge–region-based level set

Cited by 5 publications

References 42 publications

Deep Learning Transfer Methods for Biomedical Classification of Images

Deep Learning Transfer Methods for Biomedical Classification of Images

Unveiling the Best Edge Detection Algorithm for Brain Magnetic Resonance Imaging: A Qualitative and Quantitative Comparative Study

Nature inspired optimization algorithms for medical image segmentation: a comprehensive review

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