PMID- 31035406
OWN - NLM
STAT- MEDLINE
DCOM- 20190820
LR  - 20200225
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 19
IP  - 9
DP  - 2019 Apr 28
TI  - A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural 
      Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks.
LID - 10.3390/s19091992 [doi]
LID - 1992
AB  - Rapid classification of tumors that are detected in the medical images is of 
      great importance in the early diagnosis of the disease. In this paper, a new 
      liver and brain tumor classification method is proposed by using the power of 
      convolutional neural network (CNN) in feature extraction, the power of discrete 
      wavelet transform (DWT) in signal processing, and the power of long short-term 
      memory (LSTM) in signal classification. A CNN-DWT-LSTM method is proposed to 
      classify the computed tomography (CT) images of livers with tumors and to 
      classify the magnetic resonance (MR) images of brains with tumors. The proposed 
      method classifies liver tumors images as benign or malignant and then classifies 
      brain tumor images as meningioma, glioma, and pituitary. In the hybrid 
      CNN-DWT-LSTM method, the feature vector of the images is obtained from 
      pre-trained AlexNet CNN architecture. The feature vector is reduced but 
      strengthened by applying the single-level one-dimensional discrete wavelet 
      transform (1-D DWT), and it is classified by training with an LSTM network. Under 
      the scope of the study, images of 56 benign and 56 malignant liver tumors that 
      were obtained from Firat University Research Hospital were used and a publicly 
      available brain tumor dataset were used. The experimental results show that the 
      proposed method had higher performance than classifiers, such as K-nearest 
      neighbors (KNN) and support vector machine (SVM). By using the CNN-DWT-LSTM 
      hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor 
      classification and accuracy rate of 98.6% was achieved in the brain tumor 
      classification. We used two different datasets to demonstrate the performance of 
      the proposed method. Performance measurements show that the proposed method has a 
      satisfactory accuracy rate at the liver tumor and brain tumor classifying.
FAU - Kutlu, Huseyin
AU  - Kutlu H
AD  - Computer Using Department, Besni Vocational School, Adiyaman University, Adiyaman 
      02300, Turkey. hkutlu@adiyaman.edu.tr.
FAU - Avci, Engin
AU  - Avci E
AD  - Software Engineering Department, Technology Faculty, Firat University, Elazig 
      23000, Turkey. enginavci@firat.edu.tr.
LA  - eng
PT  - Journal Article
DEP - 20190428
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
MH  - Brain Neoplasms/*classification/diagnosis
MH  - Glioma/pathology
MH  - Humans
MH  - Liver Neoplasms/*classification/diagnosis
MH  - Meningioma/pathology
MH  - *Neural Networks, Computer
MH  - Pituitary Neoplasms/pathology
MH  - Sensitivity and Specificity
MH  - Support Vector Machine
MH  - Tomography, X-Ray Computed
MH  - *Wavelet Analysis
PMC - PMC6540219
OTO - NOTNLM
OT  - CNN
OT  - DWT
OT  - LSTM
OT  - biomedical image processing
OT  - classification of brain tumor
OT  - classification of liver tumor
OT  - computer-aided diagnosis
OT  - feature reduction
OT  - signal classification
COIS- The authors declare no conflict of interest.
EDAT- 2019/05/01 06:00
MHDA- 2019/08/21 06:00
PMCR- 2019/05/01
CRDT- 2019/05/01 06:00
PHST- 2019/03/07 00:00 [received]
PHST- 2019/04/23 00:00 [revised]
PHST- 2019/04/24 00:00 [accepted]
PHST- 2019/05/01 06:00 [entrez]
PHST- 2019/05/01 06:00 [pubmed]
PHST- 2019/08/21 06:00 [medline]
PHST- 2019/05/01 00:00 [pmc-release]
AID - s19091992 [pii]
AID - sensors-19-01992 [pii]
AID - 10.3390/s19091992 [doi]
PST - epublish
SO  - Sensors (Basel). 2019 Apr 28;19(9):1992. doi: 10.3390/s19091992.