Main Article Content
Abstract
Brain tumors are one of the most fatal disorders owing to the uncontrolled proliferation of abnormal cells inside the brain. Digital images are obtained using Magnetic Resonance Imaging (MRI), which is a medical instrument that can assist doctors and other medical personnel in assessing and diagnosing the presence and type of brain tumors. However, manual and subjective classification is time-consuming and error prone. Hence, an objective, automatic, and more reliable method is needed to classify MRI images of brain tumors. Artificial intelligence is considered appropriate to determine the type of brain tumor via MRI images to overcome the constraints of conventional testing methods. One method for performing automatic classification is the Convolutional Neural Network (CNN). This work demonstrates how the Inception Resnet v2 architecture in CNN is utilized to classify MRI brain tumors into four categories via transfer learning, namely glioma tumors, meningioma tumors, no tumors, and pituitary tumors. The accuracy value of the generated model reached 93.4% after running for 20 epochs. It infers that artificial intelligence is beneficial in identifying a brain tumor objectively to help doctors and radiologists in the medical field.
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References
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References
.K. Abd-Ellah, A.I. Awad, A.A. M. Khalaf, and H. F. A. Hamed, “A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned,” Magnetic Resonance Imaging, vol. 61, pp. 300–318, Sep. 2019, doi: 10.1016/j.mri.2019.05.028.
V.V. Priya and Shobarani, “An Efficient Segmentation Approach for Brain Tumor Detection in MRI,” Indian Journal of Science and Technology, vol. 9, no. 19, pp. 1¬–6, May 2016, doi: 10.17485/ijst/2016/v9i19/90440.
M.M. Badža and M.Č. Barjaktarović, “Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder,” Applied Sciences, vol. 11, no. 9, p. 4317, May 2021, doi: 10.3390/app11094317.
S. Iqbal, M.U. Ghani, T. Saba, and A. Rehman, “Brain Tumor Segmentation in Multi-Spectral MRI Using Convolutional Neural Networks (CNN),” Microscopy Research and Technique, vol. 81, no. 4, pp. 419–427, Jan. 2018, doi: 10.1002/jemt.22994.
D. Jayadevappa, S.S. Kumar, and D. Murty, “Medical Image Segmentation Algorithms using Deformable Models: A Review,” IETE Technical Review, vol. 28, no. 3, pp. 248–255, 2011, doi: https://doi.org/10.4103/0256-4602.81244.
M.K. Abd-Ellah, A.I. Awad, A.A.M. Khalaf, and H.F.A. Hamed, “A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned,” Magnetic Resonance Imaging, vol. 61, pp. 300–318, Sep. 2019, doi: 10.1016/j.mri.2019.05.028.
P. Afshar, K.N. Plataniotis, and A. Mohammadi, “BoostCaps: A Boosted Capsule Network for Brain Tumor Classification,” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 1075–1079, doi: 10.1109/EMBC44109.2020.9175922.
S. Kokkalla, J. Kakarla, I.B. Venkateswarlu, and M. Singh, “Three-Class Brain Tumor Classification Using Deep Dense Inception Residual Network,” Soft Computing, vol. 25, no. 13, pp. 8721–8729, Apr. 2021, doi: 10.1007/s00500-021-05748-8.
M.F.I. Soumik and M. A. Hossain, “Brain Tumor Classification with Inception Network Based Deep Learning Model Using Transfer Learning,” 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 2020, pp. 1018–1021, doi: 10.1109/TENSYMP50017.2020.9230618.
C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A Survey on Deep Transfer Learning,” in Artificial Neural Networks and Machine Learning – ICANN 2018, V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, and I. Maglogiannis, Eds., Cham, Switzerland: Springer, 2018, pp. 270–279, doi: 10.1007/978-3-030-01424-7_27.
A. Rehman, S. Naz, M.I. Razzak, F. Akram, and M. Imran, “A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning,” Circuits, Systems, and Signal Processing, vol. 39, no. 2, pp. 757–775, Sep. 2019, doi: 10.1007/s00034-019-01246-3.
N. Abiwinanda, M. Hanif, S.T. Hesaputra, A. Handayani, and T.R. Mengko, “Brain Tumor Classification Using Convolutional Neural Network,” in World Congress on Medical Physics and Biomedical Engineering 2018, L. Lhotska, L. Sukupova, I. Lacković, and G.S. Ibbott, Eds., Singapore: Springer, 2018, pp. 183–189, doi: 10.1007/978-981-10-9035-6_33.
E. Irmak, “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, vol. 45, no. 3, pp. 1015–1036, Apr. 2021, doi: 10.1007/s40998-021-00426-9.
T.A. Abir, J.A. Siraji, and E. Ahmed, “Analysis of a Novel MRI Based Brain Tumour Classification Using Probabilistic Neural Network (PNN),” International Journal of Scientific Research in Science, Engineering and Technology, vol. 4, no. 8, pp. 69–75, May 2018, doi: 10.32628/ijsrset184814.
E.-S.A. El-Dahshan, H.M. Mohsen, K. Revett, and A.-B. M. Salem, “Computer-Aided Diagnosis of Human Brain Tumor Through MRI: A Survey and A New Algorithm,” Expert Systems with Applications, vol. 41, no. 11, pp. 5526–5545, Sep. 2014, doi: 10.1016/j.eswa.2014.01.021.
C. Shorten and T.M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, no. 1, pp. 1–48, Jul. 2019, doi: 10.1186/s40537-019-0197-0.
X. Sun, J. Peng, Y. Shen, and H. Kang, “Tobacco Plant Detection in RGB Aerial Images,” Agriculture, vol. 10, no. 3, pp. 1–15, Feb. 2020, doi: 10.3390/agriculture10030057.
C. Dhaware and K.H. Wanjale, “Survey on Image Classification Methods in Image Processing,” International Journal of Computer Science Trends and Technology (IJCST), vol. 4, no. 3, pp. 246–248, May–Jun. 2016.
L.D. Nguyen, D. Lin, Z. Lin, and J. Cao, “Deep CNNs for Microscopic Image Classification by Exploiting Transfer Learning and Feature Concatenation,” 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 2018, pp. 1–5, doi: 10.1109/ISCAS.2018.8351550.
J.S. Paul, A.J. Plassard, B.A. Landman, and D. Fabbri, “Deep Learning for Brain Tumor Classification,” in Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, A. Krol and B. Gimi, Eds., Bellingham, WA, USA: SPIE - International Society for Optics and Photonics, 2017, doi: 10.1117/12.2254195.