Detection of Brain Tumor using Fine-Tuned Pre-Trained MobileNet Deep Learning Model
Keywords:
Machine Learning, CNN, Deep-Learning, Image processing, Brain tumor, MRI imagingAbstract
The second reason for deaths in the world other than Cardiovascular diseases is cancer. In the world, the sixth death happens due to this reason. A mass of tissues is formed when a group of abnormal cells combine together, which is commonly known as a tumor. The tumors can be distinguished into three different types as Cancerous, Non-cancerous, and Pre-cancerous. The most effective and painless technique used is MRI scans which are Magnetic Resonance Imaging. Just looking at the images and then trying to predict the type of tumor is a tough job and if done manually can have chances of human error.Recent advancements in technologies have enabled the utilization of these methods for detecting tumors present in the brain. In this study, we propose fine-tuning a MobileNet base model with additional The precision and accuracy of the model were enhanced by restructuring its layers. The quality of MRI images was improved using pre-processing techniques, while data augmentation increased the size of the dataset and improved the model's training. The study shows that the proposed model outperforms other models, demonstrating the potential of deep learning in detecting brain tumors.Our model outperforms other CNN models, including VGG16, Xception, ResNet50, and others, as indicated by the results.
Downloads
References
S. Deepak and P. M. Ameer, ‘‘Brain tumor classification using deep CNNfeatures via transfer learning,’’ Comput. Biol. Med., vol. 111, Aug. 2019,Art. no. 103345.
S. Paul, A. J. Plassard, B. A. Landman, and D. Fabbri, ‘‘Deep learningfor brain tumor classification,’’ Proc. SPIE, vol. 10137, Mar. 2017, Art. no. 1013710.
A. Çinar and M. Yildirim, ‘‘Detection of tumors on brain MRI images usingthe hybrid convolutional neural network architecture,’’ Med. Hypotheses,vol. 139, Jun. 2020, Art. no. 109684.
R. Chelghoum, A. Ikhlef, A. Hameurlaine, and S. Jacquir, ‘‘Transferlearning using convolutional neural network architectures for brain tumorclassification from MRI images,’’ in Proc. 16th IFIP WG 12.5 Int.Conf. Artif. Intell. Appl. Innov. (AIAI), Neos Marmaras, Greece, vol. 583,Jun. 2020, pp. 189–200.
A. Rehman, S. Naz, M. I. Razzak, F. Akram, and M. Imran, ‘‘A deeplearning-based framework for automatic brain tumors classification usingtransfer learning,’’ Circuits, Syst., Signal Process., vol. 39, no. 2,pp. 757–775, Feb. 2020.
N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, andT. R. Mengko, ‘‘Brain tumor classification using convolutional neuralnetwork,’’ in Proc. World Congr. Med. Phys. Biomed. Eng. Singapore:Springer, 2018, pp. 183–189.
Hasnain Ali Shah, Faisal Saeed, Sangseok Yun,Jun-Hyun Park, Anand Paul,And Jae-Mo Kang, “A Robust Approach for Brain Tumor Detectionin Magnetic Resonance ImagesUsing Finetuned EfficientNet”
R. Mehrotra, M. A. Ansari, R. Agrawal, and R. S. Anand, ‘‘A transferlearning approach for AI-based classification of brain tumors,’’ Mach.Learn. with Appl., vol. 2, Dec. 2020, Art. no. 100003.
J. Kang, Z. Ullah, and J. Gwak, ‘‘MRI-based brain tumor classificationusing ensemble of deep features and machine learning classifiers,’’Sensors, vol. 21, no. 6, p. 2222, Mar. 2021.
G. Latif, G. B. Brahim, D. N. F. A. Iskandar, A. Bashar, and J. Alghazo,‘‘Glioma Tumors’ classification using deep-neural-network-based featureswith SVM classifier,’’ Diagnostics, vol. 12, no. 4, p. 1018, Apr. 2022.
A. R. Khan, S. Khan, M. Harouni, R. Abbasi, S. Iqbal, and Z. Mehmood, ‘‘Brain tumor segmentation using K-means clustering and deep learningwith synthetic data augmentation for classification,’’ Microsc. Res. Technique, vol. 84, pp. 1389–1399, Feb. 2021.
Sohaib Asif, Wenhui Yi ,Qurrat Ul Ain3,Jin Hou,Tao Yi5and Jinhai Si., “Improving Effectiveness of Different DeepTransfer Learning-Based Models for DetectingBrain Tumors From MR Images”
N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, andM. Shoaib, ‘‘A deep learning model based on concatenation approach forthe diagnosis of brain tumor,’’ IEEE Access, vol. 8, pp. 55135–55144,2020.
F. J. Díaz-Pernas, M. Martínez-Zarzuela, M. Antón-Rodríguez, andD. González-Ortega, ‘‘A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network,’’Healthcare, vol. 9, no. 2, p. 153, Feb. 2021.
M. Talo, U. B. Baloglu, Ö. Yıldırım, and U. R. Acharya, ‘‘Application ofdeep transfer learning for automated brain abnormality classification usingMR images,’’ Cogn. Syst. Res., vol. 54, pp. 176–188, May 2019.
H. Yahyaoui, F. Ghazouani, and I. R. Farah, ‘‘Deep learning guided by anontology for medical images classification using a multimodal fusion,’’ inProc. Int. Congr. Adv. Technol. Eng. (ICOTEN), Jul. 2021, pp. 1–6.
M. M. Badža and M. Č. Barjaktarović, ‘‘Classification of brain tumors fromMRI images using a convolutional neural network,’’ Appl. Sci., vol. 10,no. 6, p. 1999, Mar. 2020.
P. Afshar, K. N. Plataniotis, and A. Mohammadi, ‘‘Capsule networksfor brain tumor classification based on MRI images and coarse tumorboundaries,’’ in Proc. IEEE Int. Conf. Acoust., Speech Signal Process.(ICASSP), May 2019, pp. 1368–1372.
K. R. Bhatele and S. S. Bhadauria, ‘‘Machine learning application inglioma classification: Review and comparison analysis,’’ Arch. Comput.Methods Eng., vol. 29, pp. 247–274, Apr. 2021.
M. Y. B. Murthy, A. Koteswararao, and M. S. Babu, ‘‘Adaptive fuzzydeformable fusion and optimized CNN with ensemble classification forautomated brain tumor diagnosis,’’ Biomed. Eng. Lett., vol. 12, no. 1, pp. 37–58, Feb. 2022.
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.
S. Somasundaram and R. Gobinath, ‘‘Current trends on deep learningmodels for brain tumor segmentation and detection—A review,’’ in Proc.Int. Conf. Mach. Learn., Big Data, Cloud Parallel Comput. (COMITCon), Feb. 2019, pp. 217–221.
G. Kumar, P. Kumar, and D. Kumar, ‘‘Brain tumor detection using convolutional neural network,’’ in Proc. IEEE Int. Conf. Mobile Network WirelessCommunication. (ICMNWC), Dec. 2021, pp. 1–6.
J. Kang, Z. Ullah, and J. Gwak, ‘‘MRI-based brain tumor classificationusing ensemble of deep features and machine learning classifiers,’’ Sennsors, vol. 21, no. 6, p. 2222, Mar. 2021.
R. Mehrotra, M. A. Ansari, R. Agrawal, and R. S. Anand, ‘‘A transferlearning approach for AI-based classification of brain tumors,’’ Machine Learn. with Appl., vol. 2, Dec. 2020, Art. no. 100003.
S. Grampurohit, V. Shalavadi, V. R. Dhotargavi, M. Kudari, and S. Jolad,‘‘Brain tumor detection using deep learning models,’’ in Proc. IEEE IndiaCouncil Int. Subsections Conf. (INDISCON), Oct. 2020, pp. 129–134.
R. Sangeetha, A. Mohanarathinam, G. Aravindh, S. Jayachitra, andM. Bhuvaneswari, ‘‘Automatic detection of brain tumor using deep learning algorithms,’’ in Proc. 4th Int. Conf. Electron., Commun. Aerosp. Technol. (ICECA), Nov. 2020, pp. 1–4
M. Li, L. Kuang, S. Xu, and Z. Sha, ‘‘Brain tumor detection based onmultimodal information fusion and convolutional neural network,’’ IEEEAccess, vol. 7, pp. 180134–180146, 2019.
M. S. Majib, M. M. Rahman, T. M. S. Sazzad, N. I. Khan, and S. K. Dey,‘‘VGG-SCNet: A VGG net-based deep learning framework for brain tumordetection on MRI images,’’ IEEE Access, vol. 9, pp. 116942–116952,2021.
M. A. B. Siddique, S. Sakib, M. M. R. Khan, A. K. Tanzeem, M. Chowdhury, and N. Yasmin, ‘‘Deep convolutional neural networksmodel-based brain tumor detection in brain MRI images,’’ in Proc. 4thInt. Conf. I-SMAC (IoT Social, Mobile, Anal. Cloud) (I-SMAC), Oct. 2020, pp. 909–914.
Dr. S. P. Predeep Kumar1, Dr. K. John Peter2 , Dr. C. Sahaya Kingsly3 “Analysis on Methods for Detecting Brain Tumor from MRI images” (aug 2022)
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
