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Article overview
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Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification | Dhananjay Joshi
; Kapil Kumar Nagwanshi
; Nitin S. Choubey
; Naveen Singh Rajput
; | Date: |
6 Jun 2022 | Abstract: | In today’s world of health care, brain tumor (BT) detection has become a
common occurrence. However, the manual BT classification approach is
time-consuming and only available at a few diagnostic centres. So Deep
Convolutional Neural Network (DCNN) is introduced in the medical field for
making accurate diagnoses and aiding in the patient’s treatment before surgery.
But these networks have problems such as overfitting and being unable to
extract necessary features for classification. To overcome these problems, we
developed the TDA-IPH and Convolutional Transfer learning and Visual Recurrent
learning with Elephant Herding Optimization hyper-parameter tuning (CTVR-EHO)
models for BT segmentation and classification. Initially, the Topological Data
Analysis based Improved Persistent Homology (TDA-IPH) is designed to segment
the BT image. Then, from the segmented image, features are extracted
simultaneously using TL via the AlexNet model and Bidirectional Visual Long
Short Term Memory (Bi-VLSTM). Elephant Herding Optimization (EHO) is used to
tune the hyper parameters of both networks to get an optimal result. Finally,
extracted features are concatenated and classified using the softmax activation
layer. The simulation result of this proposed CTVR-EHO and TDA-IPH method is
analysed based on some metrics such as precision, accuracy, recall, loss, and F
score. When compared to other existing BT segmentation and classification
models, the proposed CTVR-EHO and TDA-IPH approaches show high accuracy
(99.8%), high recall (99.23%), high precision (99.67%), and high F score
(99.59%). | Source: | arXiv, 2207.13021 | Services: | Forum | Review | PDF | Favorites |
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