Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Automated Brain Tumour Detection and Classification using Deep Features and Bayesian Optimised Classifiers

Author(s): S. Arun Kumar* and S. Sasikala

Volume 20, 2024

Published on: 23 May, 2023

Article ID: e280323215032 Pages: 14

DOI: 10.2174/1573405620666230328092218

Price: $65

Abstract

Purpose: Brain tumour detection and classification require trained radiologists for efficient diagnosis. The proposed work aims to build a Computer Aided Diagnosis (CAD) tool to automate brain tumour detection using Machine Learning (ML) and Deep Learning (DL) techniques.

Materials and Methods: Magnetic Resonance Image (MRI) collected from the publicly available Kaggle dataset is used for brain tumour detection and classification. Deep features extracted from the global pooling layer of Pretrained Resnet18 network are classified using 3 different ML Classifiers, such as Support vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT). The above classifiers are further hyperparameter optimised using Bayesian Algorithm (BA) to enhance the performance. Fusion of features extracted from shallow and deep layers of the pretrained Resnet18 network followed by BA-optimised ML classifiers is further used to enhance the detection and classification performance. The confusion matrix derived from the classifier model is used to evaluate the system's performance. Evaluation metrics, such as accuracy, sensitivity, specificity, precision, F1 score, Balance Classification Rate (BCR), Mathews Correlation Coefficient (MCC) and Kappa Coefficient (Kp), are calculated.

Results: Maximum accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC, and Kp of 99.11%, 98.99%, 99.22%, 99.09%, 99.09%, 99.10%, 98.21%, 98.21%, respectively, were obtained for detection using fusion of shallow and deep features of Resnet18 pretrained network classified by BA optimized SVM classifier. Feature fusion performs better for classification task with accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC and Kp of 97.31%, 97.30%, 98.65%, 97.37%, 97.34%, 97.97%, 95.99%, 93.95%, respectively.

Conclusion: The proposed brain tumour detection and classification framework using deep feature extraction from Resnet 18 pretrained network in conjunction with feature fusion and optimised ML classifiers can improve the system performance. Henceforth, the proposed work can be used as an assistive tool to aid the radiologist in automated brain tumour analysis and treatment.

[1]
King GG. Brain tumour classification: A comprehensive systematic review on various constraints. Comput Methods Biomech Biomed Eng Imaging Vis 2022; 1-3.
[2]
Sharma R, Aashima , Nanda M, et al. Mapping cancer in africa: A comprehensive and comparable characterization of 34 cancer types using estimates from GLOBOCAN 2020. Front Public Health 2022; 10: 839835.
[http://dx.doi.org/10.3389/fpubh.2022.839835] [PMID: 35548083]
[3]
Arabahmadi M, Farahbakhsh R, Rezazadeh J. Deep learning for smart healthcare-A survey on brain tumour detection from medical imaging. Sensors 2022; 22(5): 1960.
[http://dx.doi.org/10.3390/s22051960] [PMID: 35271115]
[4]
Mzoughi H, Njeh I, Slima MB, Hamida AB. Review of computer aided-diagnosis (CAD) systems for MRI gliomas brain tumours explorations based on machine learning and deep learning. In2022 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). 1-6.
[5]
Nazir M, Shakil S, Khurshid K. Role of deep learning in brain tumour detection and classification (2015 to 2020): A review. Comput Med Imaging Graph 2021; 91: 101940.
[http://dx.doi.org/10.1016/j.compmedimag.2021.101940] [PMID: 34293621]
[6]
Devi PR, Vemuru S. Brain tumour detection with feature extraction and tumour cell classification model using machine learning–A survey. 2022 International Conference on Electronics and Renewable Systems (ICEARS). 1250-9.
[http://dx.doi.org/10.1109/ICEARS53579.2022.9752080]
[7]
Bharathi M, Kumar SA, Sasikala S. Deep learning architectures for improving effectiveness of Covid detection–A pilot study. 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). 1-5.
[http://dx.doi.org/10.1109/ICAECA52838.2021.9675714]
[8]
Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning. Proc IEEE 2021; 109(1): 43-76.
[http://dx.doi.org/10.1109/JPROC.2020.3004555]
[9]
Sethy PK, Behera SK. A data constrained approach for brain tumour detection using fused deep features and SVM. Multimedia Tools Appl 2021; 80(19): 28745-60.
[http://dx.doi.org/10.1007/s11042-021-11098-2]
[10]
Ragab DA, Attallah O, Sharkas M, Ren J, Marshall S. A framework for breast cancer classification using Multi-DCNNs. Comput Biol Med 2021; 131: 104245.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104245] [PMID: 33556893]
[11]
Swati ZNK, Zhao Q, Kabir M, et al. Brain tumour classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph 2019; 75: 34-46.
[http://dx.doi.org/10.1016/j.compmedimag.2019.05.001] [PMID: 31150950]
[12]
Vankdothu R, Hameed MA, Fatima H. A brain tumour identification and classification using deep learning based on CNN-LSTM method. Comput Electr Eng 2022; 101: 107960.
[http://dx.doi.org/10.1016/j.compeleceng.2022.107960]
[13]
Das A, Mohapatra SK, Mohanty MN. Design of deep ensemble classifier with fuzzy decision method for biomedical image classification. Appl Soft Comput 2022; 115: 108178.
[http://dx.doi.org/10.1016/j.asoc.2021.108178]
[14]
Amin J, Anjum MA, Sharif M, Jabeen S, Kadry S, Moreno Ger P. A new model for brain tumour detection using ensemble transfer learning and quantum variational classifier. Comput Intell Neurosci 2022; 2022: 1-13.
[http://dx.doi.org/10.1155/2022/3236305] [PMID: 35463245]
[15]
Rani S, Ghai D, Kumar S, Kantipudi MVVP, Alharbi AH, Ullah MA. Efficient 3D alexnet architecture for object recognition using syntactic patterns from medical images. Comput Intell Neurosci 2022; 2022: 1-19.
[http://dx.doi.org/10.1155/2022/7882924] [PMID: 35634047]
[16]
Kibriya H, Amin R, Alshehri AH, Masood M, Alshamrani SS, Alshehri A. A novel and effective brain tumour classification model using deep feature fusion and famous machine learning classifiers. Comput Intell Neurosci 2022; 2022: 1-15.
[http://dx.doi.org/10.1155/2022/7897669] [PMID: 35378808]
[17]
Senan EM, Jadhav ME, Rassem TH, Aljaloud AS, Mohammed BA, Al-Mekhlafi ZG. Early diagnosis of brain tumour mri images using hybrid techniques between deep and machine learning. Comput Math Methods Med 2022; 2022: 1-17.
[http://dx.doi.org/10.1155/2022/8330833] [PMID: 35633922]
[18]
Alsaif H, Guesmi R, Alshammari BM, et al. A novel data augmentation-based brain tumour detection using convolutional neural network. Appl Sci 2022; 12(8): 3773.
[http://dx.doi.org/10.3390/app12083773]
[19]
Shwetha V, Madhavi CR, Nagendra Kumar M. Classification of brain tumours using hybridized convolutional neural network in brain MRI images. International Journal of Circuits, Systems and Signal Processing 2022; 16: 561-70.
[20]
Nayak DR, Padhy N, Mallick PK, Bagal DK, Kumar S. Brain tumour classification using noble deep learning approach with parametric optimization through metaheuristics approaches. Computers 2022; 11(1): 10.
[http://dx.doi.org/10.3390/computers11010010]
[21]
Latif G, Ben Brahim G, Iskandar DNFA, Bashar A, Alghazo J. Glioma tumours’ classification using deep-neural-network-based features with SVM classifier. Diagnostics 2022; 12(4): 1018.
[http://dx.doi.org/10.3390/diagnostics12041018] [PMID: 35454066]
[22]
Irmak E. Multi-classification of brain tumour MRI images using deep convolutional neural network with fully optimized framework. Iran J Sci Technol Trans Electr Eng 2021; 45(3): 1015-36.
[http://dx.doi.org/10.1007/s40998-021-00426-9]
[23]
Kibriya H, Masood M, Nawaz M, Nazir T. Multiclass classification of brain tumours using a novel CNN architecture. Multimedia Tools Appl 2022; 81(21): 29847-63.
[http://dx.doi.org/10.1007/s11042-022-12977-y]
[24]
Kang J, Ullah Z, Gwak J. Mri-based brain tumour classification using ensemble of deep features and machine learning classifiers. Sensors 2021; 21(6): 2222.
[http://dx.doi.org/10.3390/s21062222] [PMID: 33810176]
[25]
Alanazi MF, Ali MU, Hussain SJ, et al. Brain tumour/mass classification framework using magnetic-resonance-imaging-based isolated and developed transfer deep-learning model. Sensors 2022; 22(1): 372.
[http://dx.doi.org/10.3390/s22010372] [PMID: 35009911]
[26]
Zahoor MM, Qureshi SA, Bibi S, et al. A new deep hybrid boosted and ensemble learning-based brain tumour analysis using MRI. Sensors 2022; 22(7): 2726.
[http://dx.doi.org/10.3390/s22072726] [PMID: 35408340]
[27]
Seetha J, Raja SS. Brain tumour classification using convolutional neural networks. Biomed Pharmacol J 2018; 11(3): 1457-61.
[http://dx.doi.org/10.13005/bpj/1511]
[28]
Das S, Aranya OR, Labiba NN. Brain tumour classification using convolutional neural network. 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). 1-5.
[http://dx.doi.org/10.1109/ICASERT.2019.8934603]
[29]
Vashishtha P. Brain tumour classification. 2021. Available From: https://www.kaggle.com/code/purvitsharma/brain-tumour-classification-98-4-accuracy/data
[30]
Bhuvaji S, Kadam A, Bhumkar P, Dedge S, Kanchan S. Brain tumour classification (MRI). 2020.
[http://dx.doi.org/10.34740/KAGGLE/DSV/1183165]
[31]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 770-8.
[32]
Polat Ö, Güngen C. Classification of brain tumours from MR images using deep transfer learning. J Supercomput 2021; 77(7): 7236-52.
[http://dx.doi.org/10.1007/s11227-020-03572-9]
[33]
Ayyachamy S, Alex V, Khened M, Krishnamurthi G. Medical image retrieval using Resnet-18.Medical imaging Imaging informatics for healthcare, research, and applications. Int Soc Opt Photo 2019; Vol. 10954: p. 1095410.
[34]
Sannasi Chakravarthy SR, Rajaguru H. Automatic detection and classification of mammograms using improved extreme learning machine with deep learning. IRBM 2022; 43(1): 49-61.
[http://dx.doi.org/10.1016/j.irbm.2020.12.004]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy