Abstract
In the field of medical imaging, the classification of brain tumors based on histopathological analysis is a laborious and traditional approach. To address this issue, the use of deep learning techniques, specifically Convolutional Neural Networks (CNNs), has become a popular trend in research and development. Our proposed solution is a novel Convolutional Neural Network that leverages transfer learning to classify brain tumors in MRI images as benign or malignant with high accuracy. We evaluated the performance of our proposed model against several existing pre-trained networks, including Res-Net, Alex-Net, U-Net, and VGG-16. Our results showed a significant improvement in prediction accuracy, precision, recall, and F1-score, respectively, compared to the existing methods. Our proposed method achieved a benign and malignant classification accuracy of 99.30 and 98.40% using improved Res-Net 50. Our proposed system enhances image fusion quality and has the potential to aid in more accurate diagnoses.
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References
Badisa H, Polireddy M, Mohammed A (2019) CNN based brain tumor detection. Int J Eng Adv Technol (IJEAT) 8(4):1731–1734
Deshmukh RJ, Khule RS (2014) Brain tumor detection using artificial neural network fuzzy inference system (ANFIS). Int J Comput Appl Technol Res 3(3):150–154
Febrianto DC, Soesanti I, Nugroho HA (2020) Convolutional neural network for brain tumor detection. IOP Conf Series Mater Sci Eng 771(1):012031
Gu Y, Lu X, Yang L, Zhang B, Yu D, Zhao Y, Zhou T (2018) Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Comput Biol Med 103(1):220–231
Heba M, El-Dahshan E-S, El-Horbaty E-S, Salem A-B (2018) Classification using deep learning neural networks for brain tumors. Future Comput Inform J 3(1):68–71
Irmak E (2021) Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iran J Sci Technol Transact Elect Eng 2(1):1–22
Khan HA, Jue W, Mushtaq M, Mushtaq MU (2020) Brain tumor classification in MRI image using convolutional neural network. Math Biosci Eng 17(1):6203
Khotanlou H, Colliot O, Atif J, Bloch I (2009) 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 160(10):1457–1473
Kumar S, Rani S, Jain A, Verma C, Raboaca MS, Illés Z, Neagu BC (2022a) Face spoofing, age, gender and facial expression recognition using advance neural network architecture-based biometric system. Sens J 22(14):5160–5184
Kumar S, Jain A, Rani S, Alshazly H, ldris SA, Bourouis S (2022b) Deep neural network based vehicle detection and classification of aerial images. Intell Automat Soft Comput 34(1):119–131
Kumar Choudhary S, Singh K (2016b) Temporal information processing and stability analysis of the MHSN neuron model in DDF. Int J Interact Multimed Artif Intell 4(1):40–45
Kumar Choudhary S, Singh K, Solanki VK (2016a) Spiking activity of a neuron in distributed delay framework. Int J Interact Multimed Artif Intell 3(1):70–76
Lifang W, Zhang J, Liu Y, Mi J, Zhang J (2021) Multimodal medical image fusion based on gabor representation combination of multi-CNN and fuzzy neural network. IEEE Access 9:67634–67647
Mehrotra R, Ansari MA, Agrawal R, Anand RS (2020) A transfer learning approach for AI-based classification of brain tumors. Mach Learn Appl 2(1):100003
Nitish Z, Pawar V (2012) GLCM textural features for brain tumor classification. Int J Comput Sci Issues (IJCSI) 9(3):354
Ouseph NC, Shruti K (2017) A reliable method for brain tumor detection using CNN technique. IOSR J Electr Electron Eng 1(1):64–68
Rani S, Ghai D, Kumar S, Kantipudi MP, Alharbi AH, Ullah MA (2022) Efficient 3D AlexNet architecture for object recognition using syntactic patterns from medical images. Comput Intell Neurosci. https://doi.org/10.1155/2022/7882924
Shankar V, Singh K (2019) An intelligent scheme for continuous authentication of smartphone using deep auto encoder and softmax regression model easy for user brain. IEEE Access 7(1):48645–48654
Tonmoy H, Shishir FS Ashraf M, Al Nasim MA, Shah FM (2019) Brain tumor detection using convolutional neural network. In 1st IEEE International conference on advances in science, engineering and robotics technology (ICASERT), pp. 1–6
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 62(6):1609–1618
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The work is developed in Secure Computing Laboratory, School of Computer and Systems Sciences, JNU, New Delhi.
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SK and SC: wrote the main manuscript text and AJ, KS: validate the data and AA: supervised and checked final version of article and MYB: checked the final version.
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Kumar, S., Choudhary, S., Jain, A. et al. Brain Tumor Classification Using Deep Neural Network and Transfer Learning. Brain Topogr 36, 305–318 (2023). https://doi.org/10.1007/s10548-023-00953-0
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DOI: https://doi.org/10.1007/s10548-023-00953-0