Abstract
Early diagnosis of brain tumor using magnetic resonance imaging (MRI) is vital for timely medication and effective treatment. But, most people living in remote areas do not have access to medical experts and diagnosis facilities. Nevertheless, recent advancement in the Internet of Thing and artificial intelligence is transforming the healthcare system and has led to the development of the Internet of Medical Things (IoMT). An automated brain tumor classification system integrated with the IoMT framework can aid in remotely diagnosing brain tumors. However, the existing methods for brain tumor classification in MRI based on traditional machine learning and deep learning are compute-intensive. Deployment of these methods in the real-world clinical setup poses a serious challenge. Therefore, there is a requirement for robust and compute-efficient techniques for brain tumor classification. To this end, this paper presents a novel lightweight attention-guided convolutional neural network (AG-CNN) for brain tumor classification in magnetic resonance (MR) images. The designed architecture uses channel-attention blocks to focus on relevant regions of the image for tumor classification. Besides, AG-CNN uses skip connections via global-average pooling to fuse features from different stages. This approach helps the network extract enhanced features essential to differentiate tumor and normal brain MR images. To access the efficacy of the designed neural network, we evaluated it on four benchmark brain tumor MRI datasets. The comparison results with the existing state-of-the-art methods revealed the robustness and computational efficiency of the proposed AG-CNN model. The designed brain tumor classification pipeline can be easily deployed on a resource-constrained embedded platform and used in real-world clinical settings to quickly classify brain tumors in MR images.
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The codes and data generated and analyzed during the current study are available from the corresponding author on reasonable request.
References
Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F (2020) Global cancer observatory: cancer today. Lyon, France: international agency for research on cancer pp 1–6
Sekhar A, Biswas S, Hazra R, Sunaniya AK, Mukherjee A, Yang L (2021) Brain tumor classification using fine-tuned googlenet features and machine learning algorithms: Iomt enabled cad system. IEEE J Biomed Health Inform
Marosi C, Hassler M, Roessler K, Reni M, Sant M, Mazza E, Vecht C (2008) Meningioma. Crit Rev Oncol/Hematol 67(2):153–171
Weller M, Wick W, Aldape K, Brada M, Berger M, Pfister SM, Nishikawa R, Rosenthal M, Wen PY, Stupp R et al (2015) Glioma. Nat Rev Dis Primers 1(1):1–18
Kleihues P, Burger PC, Scheithauer BW (1993) The new who classification of brain tumours. Brain Pathol 3(3):255–268
Banan R, Hartmann C (2017) The new who 2016 classification of brain tumors-what neurosurgeons need to know. Acta Neurochir 159(3):403–418
Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme. Magn Reson Med Off J Int Soc Magn Reson Med 62(6):1609–1618
Işın A, Direkoğlu C, Şah M (2016) Review of mri-based brain tumor image segmentation using deep learning methods. Procedia Comput Sci 102:317–324
Kang J, Ullah Z, Gwak J (2021) Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors 21(6):2222
Deepak S, Ameer P (2019) Brain tumor classification using deep cnn features via transfer learning. Comput Biol Med 111:103345
Jafari M, Kasaei S (2011) Automatic brain tissue detection in mri images using seeded region growing segmentation and neural network classification. Australian J Basic Appl Sci 5(8):1066–1079
El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for mri brain images classification. Digit Signal Process 20(2):433–441
Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, Davatzikos C (2009) Mri-based classification of brain tumor type and grade using svm-rfe. In: 2009 IEEE international symposium on biomedical imaging: from nano to macro, IEEE, pp 1035–1038
Saritha M, Joseph KP, Mathew AT (2013) Classification of mri brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognit Lett 34(16):2151–2156
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
Mohsen H, El-Dahshan ESA, El-Horbaty ESM, Salem ABM (2018) Classification using deep learning neural networks for brain tumors. Fut Comput Inform J 3(1):68–71
Ayadi W, Elhamzi W, Charfi I, Atri M (2019) A hybrid feature extraction approach for brain mri classification based on bag-of-words. Biomed Signal Process Control 48:144–152
Ayadi W, Charfi I, Elhamzi W, Atri M (2020) Brain tumor classification based on hybrid approach. The Visual Computer pp 1–11
Anjum S, Hussain L, Ali M, Abbasi AA (2020) Automated multi-class brain tumor types detection by extracting rica based features and employing machine learning techniques. In: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, Springer, pp 249–258
Ghahfarrokhi SS, Khodadadi H (2020) Human brain tumor diagnosis using the combination of the complexity measures and texture features through magnetic resonance image. Biomed Signal Process Control 61:102025
Amin J, Sharif M, Raza M, Saba T, Anjum MA (2019) Brain tumor detection using statistical and machine learning method. Comput Methods Progr Biomed 177:69–79
Kokkalla S, Kakarla J, Venkateswarlu IB, Singh M (2021) Three-class brain tumor classification using deep dense inception residual network. Soft Comput 25(13):8721–8729
Ma L, Zhang F (2021) End-to-end predictive intelligence diagnosis in brain tumor using lightweight neural network. Appl Soft Comput 111:107666
Bashir-Gonbadi F, Khotanlou H (2021) Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach. Multimed Tools Appl 80(13):19909–19929
Masood M, Nazir T, Nawaz M, Mehmood A, Rashid J, Kwon HY, Mahmood T, Hussain A (2021) A novel deep learning method for recognition and classification of brain tumors from mri images. Diagnostics 11(5):744
Isunuri BV, Kakarla J (2021) Three-class brain tumor classification from magnetic resonance images using separable convolution based neural network. Concurrency and Computation: Practice and Experience p e6541
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
Kakarla J, Isunuri BV, Doppalapudi KS, Bylapudi KSR (2021) Three-class classification of brain magnetic resonance images using average-pooling convolutional neural network. Int J Imaging Syst Technol
Alhassan AM, Zainon WMNW (2021) Brain tumor classification in magnetic resonance image using hard swish-based relu activation function-convolutional neural network. Neural Computing and Applications pp 1–13
Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. IEEE Access 7:69215–69225
Woźniak M, Siłka J, Wieczorek M (2021) Deep neural network correlation learning mechanism for ct brain tumor detection. Neural Computing and Applications pp 1–16
Bodapati JD, Shaik NS, Naralasetti V, Mundukur NB (2021) Joint training of two-channel deep neural network for brain tumor classification. Signal Image Video Process 15(4):753–760
Kumar RL, Kakarla J, Isunuri BV, Singh M (2021) Multi-class brain tumor classification using residual network and global average pooling. Multimed Tools Appl 80(9):13429–13438
Irmak E (2021) Multi-classification of brain tumor mri images using deep convolutional neural network with fully optimized framework. Iranian Journal of Science and Technology, Transactions of Electrical Engineering pp 1–22
Díaz-Pernas FJ, Martínez-Zarzuela M, Antón-Rodríguez M, González-Ortega D (2021) A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. In: Healthcare, Multidisciplinary Digital Publishing Institute, vol 9, p 153
Liu D, Liu Y, Dong L (2019) G-resnet: Improved resnet for brain tumor classification. In: international conference on neural information processing, Springer, pp 535–545
Deepak S, Ameer P (2020) Automated categorization of brain tumor from mri using cnn features and svm. J Ambient Intell Human Comput pp 1–13
Gu X, Shen Z, Xue J, Fan Y, Ni T (2021) Brain tumor mr image classification using convolutional dictionary learning with local constraint. Frontiers in Neuroscience 15
Panwar SA, Munot MV, Gawande S, Deshpande PS (2021) A reliable and an efficient approach for diagnosis of brain tumor using transfer learning. Biomed Pharmacol J 14(1):283–294
Anjum S, Hussain L, Ali M, Alkinani MH, Aziz W, Gheller S, Abbasi AA, Marchal AR, Suresh H, Duong TQ (2021) Detecting brain tumors using deep learning convolutional neural network with transfer learning approach. International Journal of Imaging Systems and Technology
Tandel GS, Tiwari A, Kakde O (2021) Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification. Computers in Biology and Medicine p 104564
Polat Ö, Güngen C (2021) Classification of brain tumors from mr images using deep transfer learning. The Journal of Supercomputing pp 1–17
Lu SY, Wang SH, Zhang YD (2020) A classification method for brain mri via mobilenet and feedforward network with random weights. Pattern Recognit Lett 140:252–260
Noreen N, Palaniappan S, Qayyum A, Ahmad I, Imran M, Shoaib M (2020) A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 8:55135–55144
Kaur T, Gandhi TK (2020) Deep convolutional neural networks with transfer learning for automated brain image classification. Mach Vis Appl 31(3):1–16
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
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
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), IEEE, pp 314–319
Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernet Biomed Eng 39(1):63–74
Rammurthy D, Mahesh P (2020) Whale harris hawks optimization based deep learning classifier for brain tumor detection using mri images. Journal of King Saud University-Computer and Information Sciences
Huang Z, Xu H, Su S, Wang T, Luo Y, Zhao X, Liu Y, Song G, Zhao Y (2020) A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network. Comput Biol Med 121:103818
Sert E, Özyurt F, Doğantekin A (2019) A new approach for brain tumor diagnosis system: single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network. Med Hypotheses 133:109413
Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF (2018) Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP J Image Video Process 1:1–10
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
Verma K, Khunteta A (2017) Facial expression recognition using gabor filter and multi-layer artificial neural network. 2017 international conference on information. Communication, Instrumentation and Control (ICICIC), IEEE, pp 1–5
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929
Ling H, Wu J, Huang J, Chen J, Li P (2020) Attention-based convolutional neural network for deep face recognition. Multimed Tools Appl 79(9):5595–5616
Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19
Cheng J (2017) brain tumor dataset. https://doi.org/10.6084/m9.figshare.1512427.v5, https://figshare.com/articles/dataset/brain_tumor_dataset/1512427
Chakrabarty N (2019) Brain MRI Images for Brain Tumor Detection Dataset. https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection, Accessed date: September 2021
Hamada A (2020) Br35H Brain Tumor Detection 2020 Dataset. https://www.kaggle.com/ahmedhamada0/brain-tumor-detection, Accessed date: September 2021
Sartaj B, Ankita K, Prajakta B, Sameer D (2020) Brain Tumor Classification (MRI) Dataset. https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri, Accessed date: September 2021
Nirthika R, Manivannan S, Ramanan A, Wang R (2022) Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study. Neural Computing and Applications pp 1–27
Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Advances in neural information processing systems 30
Deepika J, Rajan C, Senthil T (2022) Improved capsnet model with modified loss function for medical image classification. Signal, Image and Video Processing pp 1–9
Acknowledgements
The authors would like to thank the Director, CSIR-CEERI, Pilani, for providing the necessary infrastructure and technical support. We would also like to acknowledge the consistent encouragement and motivation by the Head of the Intelligent Systems Group (ISG) at CSIR-CEERI, Pilani.
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Saurav, S., Sharma, A., Saini, R. et al. An attention-guided convolutional neural network for automated classification of brain tumor from MRI. Neural Comput & Applic 35, 2541–2560 (2023). https://doi.org/10.1007/s00521-022-07742-z
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DOI: https://doi.org/10.1007/s00521-022-07742-z