MAG-Net: Mutli-task attention guided network for brain tumor segmentation and classification

Brain tumor is the most common and deadliest disease that can be found in all age groups. Generally, MRI modality is adopted for identifying and diagnosing tumors by the radiologists. The correct identification of tumor regions and its type can aid to diagnose tumors with the followup treatment plans. However, for any radiologist analysing such scans is a complex and time-consuming task. Motivated by the deep learning based computer-aided-diagnosis systems, this paper proposes multi-task attention guided encoder-decoder network (MAG-Net) to classify and segment the brain tumor regions using MRI images. The MAG-Net is trained and evaluated on the Figshare dataset that includes coronal, axial, and sagittal views with 3 types of tumors meningioma, glioma, and pituitary tumor. With exhaustive experimental trials the model achieved promising results as compared to existing state-of-theart models, while having least number of training parameters among other state-of-the-art models.

[1]  Loïc Le Folgoc,et al.  Attention U-Net: Learning Where to Look for the Pancreas , 2018, ArXiv.

[2]  Antonio J. Plaza,et al.  Image Segmentation Using Deep Learning: A Survey , 2021, IEEE transactions on pattern analysis and machine intelligence.

[3]  Geoffrey E. Hinton,et al.  Matrix capsules with EM routing , 2018, ICLR.

[4]  P. M. Ameer,et al.  Brain tumor classification using deep CNN features via transfer learning , 2019, Comput. Biol. Medicine.

[5]  Cem Direkoglu,et al.  Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods , 2016 .

[6]  Narinder Singh Punn,et al.  CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images , 2020, Neural Processing Letters.

[7]  Mert R. Sabuncu,et al.  Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.

[8]  Hedieh Sajedi,et al.  Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines , 2018, 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE).

[9]  Qianjin Feng,et al.  Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition , 2015, PloS one.

[10]  Sonali Agarwal,et al.  Modality specific U-Net variants for biomedical image segmentation: a survey , 2021, Artificial Intelligence Review.

[11]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[12]  Ikhlas Abdel-Qader,et al.  Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network , 2018, 2018 IEEE International Conference on Electro/Information Technology (EIT).

[13]  Christopher Joseph Pal,et al.  The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.

[14]  Sonali Agarwal,et al.  Inception U-Net Architecture for Semantic Segmentation to Identify Nuclei in Microscopy Cell Images , 2020, ACM Trans. Multim. Comput. Commun. Appl..

[15]  Thamer M. Jamel,et al.  IMPLEMENTATION OF A SIGMOID ACTIVATION FUNCTION FOR NEURAL NETWORK USING FPGA , .

[16]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  F. Díaz-Pernas,et al.  A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network , 2021, Healthcare.

[18]  Su Ruan,et al.  A review: Deep learning for medical image segmentation using multi-modality fusion , 2019, Array.

[19]  Sonali Agarwal,et al.  Multi-modality encoded fusion with 3D inception U-net and decoder model for brain tumor segmentation , 2020, Multimedia Tools and Applications.

[20]  Tareq Abed Mohammed,et al.  Understanding of a convolutional neural network , 2017, 2017 International Conference on Engineering and Technology (ICET).

[21]  Konstantinos N. Plataniotis,et al.  Brain Tumor Type Classification via Capsule Networks , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[22]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.