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TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition

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Abstract

Learning discriminate representations from images plays crucial role in medical image analysis. The attention mechanism, on the other hand, leads to breakthrough results in the computer vision field by allowing models to provide varying levels of focus across image regions. In this work, we present Tumor Aware Net, an end-to-end trainable attention based Convolutional Neural Network that learns effective representations from Magnetic Resonance (MR) images suitable for effective tumor recognition. The proposed model employs a Sparse Convolutional Denoising Autoencoder (SCDA) to project the higher dimensional MR image representations to a lower dimensional space with improved discrimination. These lower dimensional descriptors are passed through an attention module, which prioritizes tumor descriptors over the rest. Furthermore, the proposed SCDA is trained jointly with the Neural induced Support Vector Classifier (NSVC) to achieve maximum margin separation. The proposed model has been validated on several publicly available benchmark datasets for tumor recognition. Based on the outcomes of the experimental studies, we claim that the proposed model favours stability and complements the learned representations when combined with attention. Despite its simplicity in terms of model parameters, the proposed model outperforms existing models for tumor type categorization.

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Data Availability

Data sharing not applicable to this article as no datasets were generated or during the current study. Existing datasets available online for public usage are used for experimental analysis.

References

  1. Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR (2019) Brain tumor classification using convolutional neural network. In: World Congress on medical physics and biomedical engineering 2018. Springer, pp 183–189

  2. Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classification via capsule networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, pp 3129–3133

  3. Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng 39(1):63–74

    Article  Google Scholar 

  4. Arakeri MP, Reddy GRM (2015) Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images. SIViP 9 (2):409–425

    Article  Google Scholar 

  5. Asif S, Yi W, Ain QU, Hou J, Yi T, Si J (2022) Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from mr images. IEEE Access 10:34716–34730

    Article  Google Scholar 

  6. Badža MM, Barjaktarović MČ (2020) Classification of brain tumors from mri images using a convolutional neural network. Appl Sci 10(6):1999

    Article  Google Scholar 

  7. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate

  8. BİNGOL H, ALATAS B (2021) Classification of brain tumor images using deep learning methods. Turk J Sci Technol 16(1):137–143

    Google Scholar 

  9. Bodapati JD (2021) Sae-pd-seq: sequence autoencoder-based pre-training of decoder for sequence learning tasks. SIViP, 1–7

  10. Bodapati JD (2022) Stacked convolutional auto-encoder representations with spatial attention for efficient diabetic retinopathy diagnosis. Multimed Tools Appl, 1–24

  11. Bodapati JD, Naik D, Suvarna B, Naralasetti V (2022) A deep learning framework with cross pooled soft attention for facial expression recognition. Journal of The Institution of Engineers (India):, Series B, 1–11

  12. Bodapati JD, Rohith V (2021) Chxcapsnet: Deep capsule network with transfer learning for evaluating pneumonia in paediatric chest radiographs. Measurement, 110491

  13. Bodapati JD, Shaik NS, Naralasetti V (2021) Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. Journal of Ambient Intelligence and Humanized Computing

  14. Bodapati JD, Shaik NS, Naralasetti V (2021) Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. SIViP, 1–8

  15. Bodapati JD, Shaik NS, Naralasetti V, Mundukur NB (2021) Joint training of two-channel deep neural network for brain tumor classification. SIViP 15(4):753–760

    Article  Google Scholar 

  16. Bodapati JD, Shareef SN, Naralasetti V, Mundukur NB (2021) Msenet: Multi-modal squeeze-and-excitation network for brain tumor severity prediction. International Journal of Pattern Recognition and Artificial Intelligence, 2157005

  17. Bodapati JD, Srilakshmi U, Veeranjaneyulu N (2022) Fernet: a deep cnn architecture for facial expression recognition in the wild. J Inst Eng (India):, Ser B 103(2):439–448

    Google Scholar 

  18. Bodapati JD, Veeranjaneyulu N, Shareef SN, Hakak S, Bilal M, Maddikunta PKR, Jo O (2020) Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electron 9(6):914

    Article  Google Scholar 

  19. Bodapati JD, Vijay A, Veeranjaneyulu N (2020) Brain tumor detection using deep features in the latent space. Ingé,nierie des Systèmes d’Information 25:2

    Google Scholar 

  20. Br35H (2022) Kaggle dataset for brain tumor recognition. https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection. Accessed: 10 Mar 2022

  21. Cheng J (2021) Brain tumor dataset. https://figshare.com/articles/dataset/brain_tumor_dataset/1512427(2017). Accessed: 10 Apr 2021

  22. Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Wang Z, Feng Q (2015) Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one 10(10):e0140381

    Article  PubMed  PubMed Central  Google Scholar 

  23. Cheng J, Yang W, Huang M, Huang W, Jiang J, Zhou Y, Yang R, Zhao J, Feng Y, Feng Q et al (2016) Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PloS one 11(6):e0157112

    Article  PubMed  PubMed Central  Google Scholar 

  24. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (07)

  25. Deepak S, Ameer P (2020) Automated categorization of brain tumor from mri using cnn features and svm. Journal of Ambient Intelligence and Humanized Computing

  26. Deepak S, Ameer P (2021) Automated categorization of brain tumor from mri using cnn features and svm. J Ambient Intell Humanized Comput 12 (8):8357–8369

    Article  Google Scholar 

  27. Deepika K, Bodapati JD, Srihitha RK (2019) An efficient automatic brain tumor classification using lbp features and svm-based classifier. In: Proceedings of International conference on computational intelligence and data engineering, Springer, pp 163–170

  28. Ghaffari M, Sowmya A, Oliver R (2019) Automated brain tumor segmentation using multimodal brain scans: A survey based on models submitted to the brats 2012–2018 challenges. IEEE Rev Biomed Eng 13:156–168

    Article  PubMed  Google Scholar 

  29. Halder TK, Sarkar K, Mandal A, Sarkar S (2022) A novel histogram feature for brain tumor detection. Int J Inf Technol 14(4):1883–1892

    Google Scholar 

  30. Ismael MR, Abdel-Qader I (2018) Brain tumor classification via statistical features and back-propagation neural network. In: 2018 IEEE international conference on electro/information technology (EIT), IEEE, pp 0252–0257

  31. Khan MA, Ashraf I, Alhaisoni M, Damaševičius R, Scherer R, Rehman A, Bukhari SAC (2020) Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics 10(8):565

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kim Y, Ohn I, Kim D (2021) Fast convergence rates of deep neural networks for classification. Neural Netw 138:179–197

    Article  PubMed  Google Scholar 

  33. Luong M-T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation

  34. Mehrotra R, Ansari M, Agrawal R, Anand R (2020) A transfer learning approach for ai-based classification of brain tumors. Mach Learn Appl 2:100003

    Google Scholar 

  35. Mohammed (2022) Kaggle dataset for brain tumor recognition. https://www.kaggle.com/mohamedmetwalysherif/braintumordataset. Accessed: 10 Mar 2022

  36. Naoneel (2021) Kaggle dataset for brain tumor recognition. https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection(2020). Accessed: 30 Sep 2021

  37. Naralasetti V, Shaik RK, Katepalli G, Bodapati JD (2021) Deep learning models for pneumonia identification and classification based on x-ray images. Traitement du Signal 38:3

    Article  Google Scholar 

  38. Pashaei A, Sajedi H, Jazayeri N (2018) Brain tumor classification via convolutional neural network and extreme learning machines. In: 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp 314–319

  39. Paul JS, Plassard AJ, Landman BA, Fabbri D (2017) Deep learning for brain tumor classification. In: Medical Imaging 2017: Biomedical applications in molecular, structural, and functional imaging. vol 10137, International society for optics and photonics, p 1013710

  40. Ranjbarzadeh R, Kasgari AB, Ghoushchi SJ, Anari S, Naseri M, Bendechache M (2021) Brain tumor segmentation based on deep learning and an attention mechanism using mri multi-modalities brain images. Sci Rep 11 (1):1–17

    Article  Google Scholar 

  41. Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep cnn with extensive data augmentation. J Comput Sci 30:174–182

    Article  Google Scholar 

  42. Sasank V, Venkateswarlu S (2021) Brain tumor classification using modified kernel based softplus extreme learning machine. Multimed Tools Appl 80 (9):13513–13534

    Article  Google Scholar 

  43. Sharif MI, Li JP, Khan MA, Saleem MA (2020) Active deep neural network features selection for segmentation and recognition of brain tumors using mri images. Pattern Recogn Lett 129:181–189

    Article  ADS  Google Scholar 

  44. Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J (2019) Brain tumor classification for mr images using transfer learning and fine-tuning. Comput Med Imaging Graph 75:34–46

    Article  PubMed  Google Scholar 

  45. Thayumanavan M, Ramasamy A (2021) An efficient approach for brain tumor detection and segmentation in mr brain images using random forest classifier. Concurr Eng 29(3):266–274

    Article  Google Scholar 

  46. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  47. Wang H, Zhang D, Ding S, Gao Z, Feng J, Wan S (2021) Rib segmentation algorithm for x-ray image based on unpaired sample augmentation and multi-scale network. Neural Comput Applic, 1–15

  48. Wang L, Zhen H, Fang X, Wan S, Ding W, Guo Y (2019) A unified two-parallel-branch deep neural network for joint gland contour and segmentation learning. Futur Gener Comput Syst 100:316–324

    Article  Google Scholar 

  49. Xie X (2018) A k-nearest neighbor technique for brain tumor segmentation using minkowski distance. J Med Imaging Health Infor 8(2):180–185

    Article  Google Scholar 

  50. Zhao Y, Li H, Wan S, Sekuboyina A, Hu X, Tetteh G, Piraud M, Menze B (2019) Knowledge-aided convolutional neural network for small organ segmentation. IEEE J Biomed Health Infor 23(4):1363–1373

    Article  Google Scholar 

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Authors and Affiliations

  1. VFSTR Deemed to be University, Guntur, 522213, India

    Jyostna Devi Bodapati

  2. IEEE Member, Bengaluru, India

    Bharadwaj Bagepalli Balaji

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  1. Jyostna Devi Bodapati
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  2. Bharadwaj Bagepalli Balaji
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Bodapati, J.D., Balaji, B.B. TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition. Multimed Tools Appl 83, 22099–22117 (2024). https://doi.org/10.1007/s11042-023-15557-w

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