Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model
DOI:
https://doi.org/10.4108/eetpht.10.6377Keywords:
CNN, Brain Tumor, MRI, Transfer Learning, Inception-V3, CNN-AlexNet, VGG16, VGG19Abstract
INTRODUCTION: Brain tumors have become a major global health concern, characterized by the abnormal growth of brain cells that can negatively affect surrounding tissues. These cells can either be malignant (cancerous) or benign (non-cancerous), with their impact varying based on their location, size and type.
OBJECTIVE: Early detection and classification of brain tumors are challenging due to their complex and variable structural makeup. Accurate early diagnosis is crucial to minimize mortality rates.
METHOD: To address this challenge, researchers proposed an optimized model based on Convolutional Neural Networks (CNNs) with transfer learning, utilizing architectures like Inception-V3, AlexNet, VGG16, and VGG19. This study evaluates the performance of these adjusted CNN models for brain tumor identification and classification using MRI data. The TCGA-LGG and The TCIA, two well-known open-source datasets, were employed to assess the model's performance. The optimized CNN architecture leveraged pre-trained weights from large image datasets through transfer learning.
RESULTS: The refined ResNet50-152 model demonstrated impressive performance metrics: for the non-tumor class, it achieved a precision of 0.98, recall of 0.95, F1 score of 0.93, and accuracy of 0.94; for the tumor class, it achieved a precision of 0.87, recall of 0.92, F1 score of 0.88, and accuracy of 0.96.
CONCLUSION: These results indicate that the refined CNN model significantly improves accuracy in classifying brain tumors from MRI scans, showcasing its potential for enhancing early diagnosis and treatment planning.
Downloads
References
Asiri, A.A.; Aamir, M.; Shaf, A.; Ali, T.; Zeeshan, M.; Irfan, M.; Alshamrani, K.A.; Alshamrani, H.A.; Alqahtani, F.F.; Alshehri,
A.H.D. Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images. Comput. Mater. Contin. 2022, 73, 5735–5753. DOI: https://doi.org/10.32604/cmc.2022.031747
Asiri, A.A.; Shaf, A.; Ali, T.; Aamir, M.; Usman, A.; Irfan, M.; Alshamrani, H.A.; Mehdar, K.M.; Alshehri, O.M.; Alqhtani, S.M. Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images. Intell. Autom. Soft Comput. 2023, 36, 127–143. DOI: https://doi.org/10.32604/iasc.2023.032391
Asiri, A.A.; Ali, T.; Shaf, A.; Aamir, M.; Shoaib, M.; Irfan, M.; Alshamrani, H.A.; Alqahtani, F.F.; Alshehri, O.M. A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI. Comput. Mater. Contin. 2022, 73, 3983–4002 DOI: https://doi.org/10.32604/cmc.2022.030923
Goding Sauer, A.; Siegel, R.L.; Jemal, A.; Fedewa, S.A. Current prevalence of major cancer risk factors and screening test use in the United States: Disparities by education and race/ethnicity. Cancer Epidemiol. Prev. Biomark. 2019, 28, 629–642. DOI: https://doi.org/10.1158/1055-9965.EPI-18-1169
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2015. CA Cancer J. Clin. 2015, 65, 5–29. DOI: https://doi.org/10.3322/caac.21254
Abiwinanda, N.; Hanif, M.; Hesaputra, S.T.; Handayani, A.; Mengko, T.R. Brain tumor classification using convolutional neural network. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering 2018, Prague, Czech Republic, 3–8 June 2018; Springer: Singapore, 2019. DOI: https://doi.org/10.1007/978-981-10-9035-6_33
Naseer, A.; Rani, M.; Naz, S.; Razzak, M.I.; Imran, M.; Xu, G. Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput. Appl. 2020, 32, 839–854.
Ostrom, Q.T.; Gittleman, H.; Truitt, G.; Boscia, A.; Kruchko, C.; Barnholtz-Sloan, J.S. CBTRUS Statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2011–2015. Neuro. Oncol. 2018, 20 (Suppl. S4), iv1–Abir, T.A.; Siraji, J.A.; Ahmed, E.; Khulna, B. Analysis of a novel MRI based brain tumour classification using probabilistic neural network (PNN). Int. J. Sci. Res. Sci. Eng. Technol. 2018, 4, 65–79. DOI: https://doi.org/10.1093/neuonc/noy131
Naseer, A.; Rani, M.; Naz, S.; Razzak, M.I.; Imran, M.; Xu, G. Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput. Appl. 2020, 32, 839–854. DOI: https://doi.org/10.1007/s00521-019-04069-0
Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015. DOI: https://doi.org/10.1109/CVPR.2015.7298965
Cheng, J. Brain Tumor Dataset. Figshare. Dataset. 2017. Available online:
https://figshare.com/articles/braintumordataset/1512427 (accessed on 27 January 2022).
Pedano, N.; Flanders, A.E.; Scarpace, L.; Mikkelsen, T.; Eschbacher, J.M.; Hermes, B.; Sisneros, V.; Barnholtz-Sloan, J.; Ostrom, Q. Radiology Data from The Cancer Genome Atlas Low Grade Glioma [TCGA-LGG] collection. Cancer Imaging Arch. 2016.
Clark, K.; Vendt, B.; Smith, K.; Freymann, J.; Kirby, J.; Koppel, P.; Moore, S.; Phillips, S.; Maffitt, D.; Pringle, M.; et al. The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J. Digit. Imaging 2013, 26, 1045–1057. DOI: https://doi.org/10.1007/s10278-013-9622-7
Cheng, Jun (2017). brain tumor dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.1512427.v5
Sartaj. Brain Tumor Classification Dataset. Available online:
https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor- classification-mri (accessed on 2 May 2023).
Nickparvar, M. Brain Tumor Classification Dataset. Available online:
https://www.kaggle.com/datasets/masoudnickparvar/ brain- tumor-mri-dataset (accessed on 2 May 2023)
Aamir, M.; Ali, T.; Shaf, A.; Irfan, M.; Saleem, M.Q. ML-DCNNet: Multi-level deep convolutional neural network for facial expression recognition and intensity estimation. Arab. J. Sci. Eng. 2020, 45, 10605–10620. DOI: https://doi.org/10.1007/s13369-020-04811-0
Jie, H.J.; Wanda, P. RunPool: A dynamic pooling layer for convolution neural network. Int. J. Comput. Intell. Syst. 2020, 13, 66–76. DOI: https://doi.org/10.2991/ijcis.d.200120.002
Vinoth, R.; Venkatesh, C. Segmentation and Detection of Tumor in MRI images Using CNN and SVM Classification. In Proceedings of the 2018 Conference on Emerging Devices and Smart Systems (ICEDSS), Tiruchengode, India, 2–3 March 2018; IEEE: Piscataway, NJ, USA, 2018. DOI: https://doi.org/10.1109/ICEDSS.2018.8544306
Rehman, A.; Naz, S.; Razzak, M.I.; Akram, F.; Imran, M. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst. Signal Process. 2020, 39, 757–775 DOI: https://doi.org/10.1007/s00034-019-01246-3
Swati, Z.N.K.; Zhao, Q.; Kabir, M.; Ali, F.; Ali, Z.; Ahmed, S.; Lu, J. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput. Med. Imaging Graph. 2019, 75, 34–46. DOI: https://doi.org/10.1016/j.compmedimag.2019.05.001
Ahmed, K.B.; Hall, L.O.; Goldgof, D.B.; Liu, R.; Gatenby, R.A. Fine-tuning convolutional deep features for MRI based brain tumor classification. In Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, Orlando, FL, USA, 3 March 2017; SPIE: Bellingham, WA, USA, 2017. DOI: https://doi.org/10.1117/12.2253982
Balaji, C. ., & Veni, S. . (2023). Automatic Skull Stripping from MRI of Human Brain using Deep Learning Framework for the Diagnosis of Brain Related Diseases. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 439–445. Retrieved from:
https://ijisae.org/index.php/IJISAE/article/view/3541
Meenakshi Sooda, Shruti Jainb*, Jyotsna Dograc.(2023) Classification and Pathologic Diagnosis of Gliomas in MR Brain Images , International Conference on Machine Learning and Data Engineering ScienceDirect comProcedia Computer Science 218 (2023) 706– 717 DOI: https://doi.org/10.1016/j.procs.2023.01.051
Seyedamid Seyedhashemi, Mehdi Esmaeili. Detecting Tumors in MRI Scans using a Convolutional Neural Network. Authorea. February 22, 2023. DOI: https://doi.org/10.22541/au.167707443.35803899/v1
Chetana Srinivas, Nandini Prasad K. S., Mohammed Zakariah, Yousef Ajmi Alothaibi Kamran Shaukat, B. Partibane, and Halifa Awal (2022) Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images, Wearable Devices for Smart Healthcare DOI: https://doi.org/10.1155/2022/3264367
Chen SC, Lo CM, Wang SH, Su EC. RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p/19q codeletion. BMC Bioinformatics. 2019 Dec 24;20 (Suppl 19):659. doi: 10.1186/s12859-019-3236-0. PMID: 31870275; PMCID: PMC6929429. DOI: https://doi.org/10.1186/s12859-019-3236-0
Bacchi S, Zerner T, Dongas J, Asahina AT, Abou-Hamden A, Otto S, Oakden-Rayner L, Patel S. Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study. J Clin Neurosci. 2019 Dec;70:11-13. doi: 10.1016/j.jocn.2019.10.003. Epub 2019 Oct 21. PMID: 31648967. DOI: https://doi.org/10.1016/j.jocn.2019.10.003
Sebastian R. van der Voort; Fatih Incekara; Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Clin Cancer Res (2019) 25 (24): 7455–7462. https://doi.org/10.1158/1078- 0432.CCR-19-1127 DOI: https://doi.org/10.1158/1078-0432.CCR-19-1127
Bangalore Yogananda CG, Shah BR, Vejdani-Jahromi M, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Mickey B, Patel TR, Fei B, Madhuranthakam AJ, Maldjian JA. A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas. Neuro Oncol. 2020 Mar 5;22(3):402-411. doi: 10.1093/neuonc/noz199. Retraction in: Neuro Oncol. 2023 Jun 2;25(6):1197. PMID: 31637430; PMCID: PMC7442388. DOI: https://doi.org/10.1093/neuonc/noz199
Saima Rathore, Muhammad Aksam Iftikhar, Zissimos Mourelatos, Prediction of overall survival and molecular markers in gliomas via analysis of digital pathology images using deep learning, Image and Video Processing, https://doi.org/10.48550/arXiv.1909.09124
Wong KK, Rostomily R, Wong STC. Prognostic Gene Discovery in Glioblastoma Patients using Deep Learning. Cancers (Basel). 2019 Jan 8;11(1):53. doi: 10.3390/cancers11010053. PMID: 30626092; PMCID: PMC6356839. DOI: https://doi.org/10.3390/cancers11010053
Chen C, Zheng A, Ou X, Wang J, Ma X. Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma from Primary Central Nervous System Lymphoma. Front Oncol. 2020 Sep 15;10:1151. doi: 10.3389/fonc.2020.01151. PMID: 33042784; PMCID: PMC7522159. DOI: https://doi.org/10.3389/fonc.2020.01151
Shrivastava, V. K., Shelke, C. J., Shrivastava, A., Mohanty, S. N., & Sharma, N. (2023). Optimized Deep Learning Model for Disease Prediction in Potato Leaves. EAI Endorsed Transactions on Pervasive Health and Technology, 9. DOI: https://doi.org/10.4108/eetpht.9.4001
Karnik, M.P., Kodavade, D.V. (2023),Abstractive Summarization with Efficient Transformer Based Approach , International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169 Volume: 11 Issue: 4 DOI: https://doi.org/10.17762/ijritcc.v11i4.6454 DOI: https://doi.org/10.17762/ijritcc.v11i4.6454
Karnik, M.P., Kodavade, D.V. (2023). A Survey on Controllable Abstractive Text Summarization. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_30 DOI: https://doi.org/10.1007/978-3-031-35507-3_30
Shrivastava, V. K., Shrivastava, A., Sharma, N., Mohanty, S. N., & Pattanaik, C. R. (2023). Deep learning model for temperature prediction: A case study in New Delhi. Journal of Forecasting. DOI: https://doi.org/10.1002/for.2966
Wankhede, D.S., Shelke, C.J. (2023). An Investigative Approach on the Prediction of Isocitrate Dehydrogenase (IDH1) Mutations and Co-deletion of 1p19q in Glioma Brain Tumors. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978- 3-031-35507-3_19 DOI: https://doi.org/10.1007/978-3-031-35507-3_19
Wankhede, D.S., Pandit, S., Metangale, N., Patre, R., Kulkarni, S., Minaj, K.A. (2022). Survey on Analyzing Tongue Images to Predict the Organ Affected. In: Hybrid Intelligent Systems. HIS 2021. Lecture Notes in Networks and Systems, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-96305-7_56 DOI: https://doi.org/10.1007/978-3-030-96305-7_56
S. Solanki, U. P. Singh, S. S. Chouhan and S. Jain, "Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview," in IEEE Access, vol. 11, pp. 12870-12886, 2023, doi: 10.1109/ACCESS.2023.3242666. DOI: https://doi.org/10.1109/ACCESS.2023.3242666
G. S. Prasad and V. S. Gaikwad, "A survey on user awareness of cloud security", Int. J. of Engineering and Technology, vol. 7, no. 2.32, pp. 131-135, May 2018. DOI: https://doi.org/10.14419/ijet.v7i2.32.15386
Gudapati S.P., Gaikwad V. (2021) Light-Weight Key Establishment Mechanism for Secure Communication Between IoT Devices and Cloud. In: Satapathy S., Bhateja V., Janakiramaiah B., Chen YW. (eds) Intelligent System Design. Advances in Intelligent Systems and Computing, vol 1171. Springer, Singapore. https://doi.org/10.1007/978-981-15-5400-1_55 DOI: https://doi.org/10.1007/978-981-15-5400-1_55
A. Shetty, A. Thorat, R. Singru, M. Shigawan and V. Gaikwad, "Predict Socio-Economic Status of an Area from Satellite Image Using Deep Learning," 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 177-182, doi: 10.1109/ICESC48915.2020.9155696. DOI: https://doi.org/10.1109/ICESC48915.2020.9155696
Shrimant, Gaikwad Vidya, Ravindranath, K. & Prasad, Gudapati Syam (2023) A mathematical model for secure Cloud-IoT communication: Introducing the revolutionary lightweight key mechanism, Journal of Discrete Mathematical Sciences and Cryptography, 26:5, 1341–1354, DOI: 10.47974/JDMSC-1750 DOI: https://doi.org/10.47974/JDMSC-1750
Shrivastava, V. K., Shrivastava, A., Sharma, N., Mohanty, S. N., & Pattanaik, C. R. (2023). Deep learning model for temperature prediction: an empirical study. Modeling Earth Systems and Environment, 9(2), 2067-2080. DOI: https://doi.org/10.1007/s40808-022-01609-x
Batra, R., Mahajan, M., Shrivastava, V. K., & Goel, A. K. (2021). Detection of COVID-19 using textual clinical data: a machine learning approach. Impact of AI and data science in response to coronavirus pandemic, 97-109. DOI: https://doi.org/10.1007/978-981-16-2786-6_5
Saini, V., Rai, N., Sharma, N., & Shrivastava, V. K. (2022, December). A Convolutional Neural Network Based Prediction Model for Classification of Skin Cancer Images. In International Conference on Intelligent Systems and Machine Learning (pp. 92-102). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-35078-8_9
Singhal, A., Phogat, M., Kumar, D., Kumar, A., Dahiya, M., & Shrivastava, V. K. (2022). Study of deep learning techniques for medical image analysis: A review. Materials Today: Proceedings, 56, 209-214. DOI: https://doi.org/10.1016/j.matpr.2022.01.071
Shrivastava, V. K., Kumar, A., Shrivastava, A., Tiwari, A., Thiru, K., & Batra, R. (2021, August). Study and trend prediction of Covid-19 cases in India using deep learning techniques. In Journal of Physics: Conference Series (Vol. 1950, No. 1, p. 012084). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/1950/1/012084
Batra, R., Shrivastava, V. K., & Goel, A. K. (2021). Anomaly Detection over SDN Using Machine Learning and Deep Learning for Securing Smart City. In Green Internet of Things for Smart Cities DOI: https://doi.org/10.1201/9781003032397-13
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Disha Sushant Wankhede, Chetan J. Shelke, Virendra Kumar Shrivastava, Rathnakar Achary, Sachi Nandan Mohanty
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
