Semantic Segmentation of MRI Images for Brain Tumour Detection with ShuffleNet-Based UNet

Potnuru, Mohankrishna; Naick, B. Suribabu

doi:10.1007/s42979-023-01878-y

Cited by 4 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the last fully connected layer with the "softmax" activation function is attached to the model, indicating the required number of classes, and its compilation is performed. The model constructed in this way can already be trained to solve a specific task [10]. Another approach is related to the need for fine tuning of the model (Fine Tuning Model) [11].…”

Section: Transfer Learning Methods In Image Recognitionmentioning

confidence: 99%

Deep Learning Transfer Methods for Biomedical Classification of Images

Al-Janabi

2024

Ijirme

View full text Add to dashboard Cite

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%.

show abstract

Section: Transfer Learning Methods In Image Recognitionmentioning

confidence: 99%