TY  - CONF
SN  - 9781538624715
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2019///
EP  - 637
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062802273&doi=10.1109%2fIECBES.2018.8626704&partnerID=40&md5=53ea5abbbd1632ed70c58c6fc43df27e
A1  - Shahzadi, I.
A1  - Meriadeau, F.
A1  - Tang, T.B.
A1  - Quyyum, A.
AV  - none
KW  - Biomedical engineering; Brain; Diagnosis; Image classification; Long short-term memory; Magnetic resonance imaging; Medical imaging; Tumors
KW  -  Classification accuracy; CNNs; Convolutional neural network; Disease progression; Glioma; High-level features; LSTM; VGG-16
KW  -  Classification (of information)
ID  - scholars11819
SP  - 633
TI  - CNN-LSTM: Cascaded framework for brain tumour classification
N2  - Glioma is common type of brain tumour in adults originating from glia cell. Despite advances in medical image analysis and gliomas research, accuarte diagnosis remains a challenge. Gliomas can be in general classifed into High Grade (HG) and Low Grade (LG). The exact classification of glioma helps in evaluating the disease progression and selection of the treatment strategy. Whilst medical image classification using a Convolutional Neural Networks (CNNs) has achieved remarkable success, but it is still difficult task for CNNs to accurately classify 3D medical images. One of the major limitation is the fact that CNNs are difficult to optimize in 3D volumetric classification. In current work, we addressed this challenge by introducing a cascade of CNN with Long Short Term Memory (LSTM) Network for classification of 3D brain tumor MR images into HG and LG glioma. Features from pre-trained VGG-16 were extracted and fed into LSTM network for learning high-level feature representations to classify the 3D brain tumour volumes into HG and LG glioma. The results showed that the features extracted from VGG-16 gave better classification accuracy as compared to the features extracted from AlexNet and ResNet. © 2018 IEEE
N1  - cited By 55; Conference of 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 ; Conference Date: 3 December 2018 Through 6 December 2018; Conference Code:144644
ER  -