nach oben

Neural Computing and Applications

Erschienen in:

13.06.2020 | S. I : Hybridization of Neural Computing with Nature Inspired Algorithms

Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm

verfasst von: Siyuan Lu, Shui-Hua Wang, Yu-Dong Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 17/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Computer-aided diagnosis system is becoming a more and more important tool in clinical treatment, which can provide a verification of the doctors’ decisions. In this paper, we proposed a novel abnormal brain detection method for magnetic resonance image. Firstly, a pre-trained AlexNet was modified with batch normalization layers and trained on our brain images. Then, the last several layers were replaced with an extreme learning machine. A searching method was proposed to find the best number of layers to be replaced. Finally, the extreme learning machine was optimized by chaotic bat algorithm to obtain better classification performance. Experiment results based on 5 × hold-out validation revealed that our method achieved state-of-the-art performance.

Vorheriger Artikel Neural image reconstruction using a heuristic validation mechanism

Nächster Artikel A hybrid approach for search and rescue using 3DCNN and PSO

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Chaplot S et al (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86–92CrossRef

El-Dahshan E-SA et al (2010) Hybrid intelligent techniques for MRI brain images classification. Digit Signal Process 20(2):433–441CrossRef

Kalbkhani H et al (2013) Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series. Biomed Signal Process Control 8(6):909–919CrossRef

Saritha M et al (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn Lett 34(16):2151–2156CrossRef

El-Dahshan E-SA et al (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl 41(11):5526–5545CrossRef

Bahadure NB et al (2018) Comparative approach of MRI-Based brain tumor segmentation and classification using genetic algorithm. J Digit Imaging 31(4):477–489CrossRef

Gudigar A et al (2019) Automated categorization of multi-class brain abnormalities using decomposition techniques with mri images: a comparative study. IEEE Access 7:28498–28509CrossRef

Acharya UR et al (2019) Automated detection of alzheimer’s disease using brain mri images- a study with various feature extraction techniques. J Med Syst 43(9):302CrossRef

Pandey HM et al (2016) Grammar induction using bit masking oriented genetic algorithm and comparative analysis. Appl Soft Comput 38:453–468CrossRef

10.

Pandey HM et al (2014) A comparative review of approaches to prevent premature convergence in GA. Appl Soft Comput 24:1047–1077

11.

Pandey HM et al (2016) Maintaining regularity and generalization in data using the minimum description length principle and genetic algorithm: case of grammatical inference. Swarm Evolut Comput 31:11–23CrossRef

12.

Pandey HM et al (2019) A comprehensive classification of deep learning librariess. In: Yang XS et al (eds) Third international congress on information and communication technology. Springer, Cham, pp 427–435CrossRef

13.

Pandey HM et al (2018) Bit mask-oriented genetic algorithm for grammatical inference and premature convergence. Int J Bio-Inspir Comput 12(1):54–69CrossRef

14.

Nayak DR et al (2019) Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images. Multimed Tools Appl

15.

Deepak S et al (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345CrossRef

16.

Han C et al (2019) Combining noise-to-image and image-to-image GANs: brain MR image augmentation for tumor detection. IEEE Access 7:156966–156977CrossRef

17.

Lu S et al (2019) Pathological brain detection based on AlexNet and transfer learning. J Comput Sci 30:41–47CrossRef

18.

Krizhevsky A et al (2012) ImageNet classification with deep convolutional neural networks. In: International conference on neural information processing systems, p. 1097–1105

19.

He K et al (2016) Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR). p 770–778

20.

Szegedy C et al (2014) Going deeper with convolutions

21.

Simonyan K et al (2015) Very deep convolutional networks for large-scale image recognition. International conference on learning representations

22.

Guang-Bin H et al (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

23.

Huang G-B et al (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163CrossRef

24.

Tang J et al (2015) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809–821MathSciNetCrossRef

25.

Wang Y et al (2011) A study on effectiveness of extreme learning machine. Neurocomputing 74(16):2483–2490CrossRef

26.

Zhang L et al (2017) Deep object recognition across domains based on adaptive extreme learning machine. Neurocomputing 239:194–203CrossRef

27.

Yang X-C et al (2015) Pressure prediction of coal slurry transportation pipeline based on particle swarm optimization kernel function extreme learning machine. Math Probl Eng 2015:1–7

28.

Liu T et al (2018) Extreme learning machine for joint embedding and clustering. Neurocomputing 277:78–88CrossRef

29.

Schmidt WF et al (1992) Feedforward neural networks with random weights, In: Proceedings., 11th IAPR international conference on pattern recognition. Vol.II. conference b: pattern recognition methodology and systems. p 1-4

30.

Pao YH et al (1994) Learning and generalization characteristics of random vector functional-link net. Neurocomputing 6:163–180CrossRef

31.

Yang X-S (2010) A new metaheuristic Bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), vol 284, pp 65–74

32.

Arasomwan AM et al (2014) An investigation into the performance of particle swarm optimization with various chaotic maps. Math Prob Eng 2014:1–17MathSciNetCrossRef

33.

Lu Z et al (2016) A pathological brain detection system based on radial basis function neural network. J Med Imaging Health Inf 6(5):1218–1222CrossRef

34.

Sajjad M et al (2019) Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 30:174–182CrossRef

Titel: Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm
verfasst von: Siyuan Lu
Shui-Hua Wang
Yu-Dong Zhang
Publikationsdatum: 13.06.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 17/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05082-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 17/2021

Classification of immature white blood cells in acute lymphoblastic leukemia L1 using neural networks particle swarm optimization

Granular multiple kernel learning for identifying RNA-binding protein residues via integrating sequence and structure information

Designing mm-wave electromagnetic engineered surfaces using generative adversarial networks

LSTM training set analysis and clustering model development for short-term traffic flow prediction

A hybrid rolling grey framework for short time series modelling

Pricing policy on a dual competitive channel for a green product under fuzzy conditions

Premium Partner