Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Health and Technology
Erschienen in: Health and Technology 6/2022

07.09.2022 | Original Paper

Brain tumor detection using deep ensemble model with wavelet features

verfasst von: Debendra Kumar Sahoo, Abhishek Das, Satyasis Mishra, Mihir Narayan Mohanty

Erschienen in: Health and Technology | Ausgabe 6/2022

Einloggen

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

search-config
loading …

Abstract

Purpose

A person's healthy activities are determined by the state of his or her brain. The brain is in charge of all of a person's activities. If a small abnormality develops in the brain, it will have a negative impact on the person regardless of whether the other organs are in good condition. As a result, early detection of any abnormal growth in the brain is essential.

Methods

In this work, the authors have utilized data pre-processing using discrete wavelet transform (DWT) and segmentation, whereas, for detection, an ensemble learning technique is proposed. DWT and segmentation help in increasing the dataset size that is used to train the deep learning model. Segmentation using supervised Auto-encoder (AE) is used for data enhancement to strengthen the training process. The original data, outputs of DWT, and segmented images are utilized for the training of the ensemble model designed with three parallel-connected convolutional neural networks (CNNs).

Results

The detection results obtained from the ensemble of these recurrent models are then passed through the Multilayer Perceptron (MLP) for final detection. Kaggle brain MRI image dataset is used to complete the proposed method. Test accuracy, F1-score, precision, sensitivity, and specificity provided by this method are 98.08%, 0.9836, 1.0000, 0.9677, and 1.0000 respectively. In comparison to state-of-the-art models, the proposed model produces competitive outcomes.

Conclusion

In time detection of the tumor may lead to the survival of the patient. Automatic and accurate detection is another perspective of this field. For this purpose, we have proposed a deep ensemble model with wavelet features. The ensemble model provides increased performance in comparison to single models due to the parallel training.
Vorheriger Artikel Breast cancer image analysis using deep learning techniques – a survey
Nächster Artikel A non-linear time series based artificial intelligence model to predict outcome in cardiac surgery

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

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

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
Literatur
1.
Mohan G, Subashini MM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control. 2018;39:139–61.CrossRef
2.
Ker J, Singh SP, Bai Y, Rao J, Lim T, Wang L. Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans. Sensors. 2019;19(9):2167.CrossRef
3.
Ker J, Bai Y, Lee HY, Rao J, Wang L. Automated brain histology classification using machine learning. J Clin Neurosci. 2019;66:239–45.CrossRef
4.
Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. Ieee Access. 2017;6:9375–89.CrossRef
5.
Bai X, Niwas SI, Lin W, Ju BF, Kwoh CK, Wang L, Sng CC, Aquino MC, Chew PT. Learning ECOC code matrix for multiclass classification with application to glaucoma diagnosis. J Med Syst. 2016;40(4):1–10.CrossRef
6.
He G, B., Cui, J., Xiao, B., and Wang, X. Image analysis by two types of Franklin-Fourier moments. IEEE/CAA Journal of Automatica Sinica. 2019;6(4):1036–51.MathSciNetCrossRef
7.
Mitra S, Shankar BU. Medical image analysis for cancer management in natural computing framework. Inf Sci. 2015;306:111–31.CrossRef
8.
Jyoti A, Mohanty MN, Kumar MP. Morphological based segmentation of brain image for tumor detection. In 2014 International Conference on Electronics and Communication Systems (ICECS) 2014;1–5. IEEE.
9.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.CrossRef
10.
Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. arXiv preprint arXiv:​1412.​3555.
11.
Das A, Patra, GR, Mohanty MN. LSTM based Odia handwritten numeral recognition. In 2020 international conference on communication and signal processing (ICCSP). 2020;0538–0541. IEEE.
12.
Jia Z, Chen D. Brain Tumor Identification and Classification of MRI images using deep learning techniques. IEEE Access. 2020.
13.
Majib MS, Rahman MM, Sazzad TS, Khan NI, Dey SK. VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images. IEEE Access. 2021;9:116942–52.CrossRef
14.
Mallick PK, Ryu SH, Satapathy SK, Mishra S, Nguyen GN, Tiwari P. Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access. 2019;7:46278–87.CrossRef
15.
Khan P, Kader MF, Islam SR, Rahman AB, Kamal MS, Toha MU, Kwak KS. Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances. IEEE Access. 2021;9:37622–55.CrossRef
16.
Çelik G, Talu MF. A new 3D MRI segmentation method based on Generative Adversarial Network and Atrous Convolution. Biomed Signal Process Control. 2022;71: 103155.CrossRef
17.
Dikici E, Ryu JL, Demirer M, Bigelow M, White RD, Slone W, Prevedello LM. Automated brain metastases detection framework for T1-weighted contrast-enhanced 3D MRI. IEEE J Biomed Health Inform. 2020;24(10):2883–93.CrossRef
18.
Takrouni W, Douik A. Improving geometric P-norm-based glioma segmentation through deep convolutional autoencoder encapsulation. Biomed Signal Process Control. 2022;71: 103232.CrossRef
19.
Noreen N, Palaniappan S, Qayyum A, Ahmad I, Imran M, Shoaib M. A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access. 2020;8:55135–44.CrossRef
20.
Li M, Kuang L, Xu S, Sha Z. Brain tumor detection based on multimodal information fusion and convolutional neural network. IEEE Access. 2019;7:180134–46.CrossRef
21.
Sekhar A, Biswas S, Hazra R, Sunaniya AK, Mukherjee A, Yang L. Brain tumor classification using fine-tuned GoogLeNet features and machine learning algorithms: IoMT enabled CAD system. IEEE J Biomed Health Inform. 2021.
22.
Hao Q, Pei Y, Zhou R, Sun B, Sun J, Li S, Kang X. Fusing Multiple Deep Models for In Vivo Human Brain Hyperspectral Image Classification to Identify Glioblastoma Tumor. IEEE Trans Instrum Meas. 2021;70:1–14.
23.
Tang Z, Yap PT, Shen D. A new multi-atlas registration framework for multimodal pathological images using conventional monomodal normal atlases. IEEE Trans Image Process. 2018;28(5):2293–304.MathSciNetCrossRef
24.
Ghassemi N, Shoeibi A, Rouhani M. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control. 2020;57: 101678.CrossRef
25.
Chandra SK, Bajpai MK. Fractional Crank-Nicolson finite difference method for benign brain tumor detection and segmentation. Biomed Signal Process Control. 2020;60: 102002.CrossRef
26.
Nema S, Dudhane A, Murala S, Naidu S. RescueNet: An unpaired GAN for brain tumor segmentation. Biomed Signal Process Control. 2020;55: 101641.CrossRef
27.
Ghahfarrokhi SS, Khodadadi H. Human brain tumor diagnosis using the combination of the complexity measures and texture features through magnetic resonance image. Biomed Signal Process Control. 2020;61: 102025.CrossRef
28.
Karayegen G, Aksahin MF. Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region. Biomed Signal Process Control. 2021;66: 102458.CrossRef
29.
Rai HM, Chatterjee K, Dashkevich S. Automatic and accurate abnormality detection from brain MR images using a novel hybrid UnetResNext-50 deep CNN model. Biomed Signal Process Control. 2021;66: 102477.CrossRef
30.
Hedayati R, Khedmati M, Taghipour-Gorjikolaie M. Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer’s disease diagnosis. Biomed Signal Process Control. 2021;66: 102397.CrossRef
31.
Aghalari M, Aghagolzadeh A, Ezoji M. Brain tumor image segmentation via asymmetric/symmetric UNet based on two-pathway-residual blocks. Biomed Signal Process Control. 2021;69: 102841.CrossRef
32.
Das A, Mohapatra SK, Mohanty MN. Design of deep ensemble classifier with fuzzy decision method for biomedical image classification. Appl Soft Comput. 2022;115: 108178.CrossRef
33.
Das A, Mohanty MN, Mallick PK, Tiwari P, Muhammad K, Zhu H. Breast cancer detection using an ensemble deep learning method. Biomed Signal Process Control. 2021;70: 103009.CrossRef
34.
Xiao L, Wan X, Han Z. PHY-layer authentication with multiple landmarks with reduced overhead. IEEE Trans Wireless Commun. 2017;17(3):1676–87.CrossRef
35.
Xiao L, Wan X, Lu X, Zhang Y, Wu D. IoT security techniques based on machine learning: How do IoT devices use AI to enhance security? IEEE Signal Process Mag. 2018;35(5):41–9.CrossRef
36.
Deng L, Platt J. Ensemble deep learning for speech recognition. In Proc. interspeech. 2014.
37.
Xu L, Jiang JH, Zhou YP, Wu HL, Shen GL, Yu RQ. MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration. Chemom Intell Lab Syst. 2007;87(2):226–30.CrossRef
38.
Sakkis G, Androutsopoulos I, Paliouras G, Karkaletsis V, Spyropoulos CD, Stamatopoulos P. Stacking classifiers for anti-spam filtering of e-mail. 2001. arXiv preprint cs/0106040.
39.
Aversano T, L., Bernardi, M. L., and Cimitile, M. Water stress classification using Convolutional Deep Neural Networks. J Univ Comput Sci. 2022;28(3):311–28.
40.
Taştimur CANAN, Akın E. Fastener Classification Using One-Shot Learning with Siamese Convolution Networks. J Univ Comput Sci. 2022;28(1).
41.
Suong LK, Kwon J. Detection of Potholes Using a Deep Convolutional Neural Network. J Univ Comput Sci. 2018;24(9):1244–1257
42.
Krstinic D, Braovic M, Bozic-Stulic D. Convolutional Neural Networks and Transfer Learning Based Classification of Natural Landscape Images. J Univ Comput Sci. 2020;26(2):244–67.
43.
Das AK, Pati SK, Huang HH, Chen CK. Cancer Classification by Gene Subset Selection from Microarray Dataset. J Univers Comput Sci. 2018;24(6):682–710.MathSciNet
44.
Demirci MERVE, Sağıroğlu ŞEREF. TwitterBulletin: An Intelligent and Real-Time Automated News Categorization Tool for Twitter. J Univ Comput Sci. 2022;28.
45.
Panda S, Das A, Mishra S, Mohanty MN. Epileptic seizure detection using deep ensemble network with empirical wavelet transform. Measurement Science Review. 2021;21(4):110–6.CrossRef
46.
Nguyen TT, Nguyen MP, Pham XC, Liew AWC. Heterogeneous classifier ensemble with fuzzy rule-based meta learner. Inf Sci. 2018;422:144–60.CrossRef
47.
Das A, Mohanty MN. Design of Encoder in CNN for Effective Recognition of Odia Numerals. In 2021 1st Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON). 2021;1–3. IEEE.
48.
49.
Huang Z, Du X, Chen L, Li Y, Liu M, Chou Y, Jin L. Convolutional neural network based on complex networks for brain tumor image classification with a modified activation function. IEEE Access. 2020;8:89281–90.CrossRef
50.
Huda S, Yearwood J, Jelinek HF, Hassan MM, Fortino G, Buckland M. A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for brain tumor diagnosis. IEEE access. 2016;4:9145–54.CrossRef
51.
Ge C, Gu IYH, Jakola AS, Yang J. Enlarged training dataset by pairwise GANs for molecular-based brain tumor classification. IEEE Access. 2020;8:22560–70.CrossRef
52.
Ertosun MG, Rubin DL. Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks. In AMIA Annual Symposium Proceedings. 2015; 2015:1899. American Medical Informatics Association.
Metadaten
Titel
Brain tumor detection using deep ensemble model with wavelet features
verfasst von
Debendra Kumar Sahoo
Abhishek Das
Satyasis Mishra
Mihir Narayan Mohanty
Publikationsdatum
07.09.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Health and Technology / Ausgabe 6/2022
Print ISSN: 2190-7188
Elektronische ISSN: 2190-7196
DOI
https://doi.org/10.1007/s12553-022-00699-y

Weitere Artikel der Ausgabe 6/2022

Health and Technology 6/2022 Zur Ausgabe

Original Paper

Combining deep-wavelet neural networks and support-vector machines to classify breast lesions in thermography images

Original Paper

A non-linear time series based artificial intelligence model to predict outcome in cardiac surgery

Original Paper

Predictive analytics of COVID-19 cases and tourist arrivals in ASEAN based on covid-19 cases

Original Paper

Hybrid deep boosting ensembles for histopathological breast cancer classification

Original Paper

Integration of artificial intelligence into nursing practice

Original Paper

Breast cancer image analysis using deep learning techniques – a survey