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Current Medical Imaging

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

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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

open_access

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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.

Keywords: Brain tumour, Resnet 18, Detection, Classification, Deep learning, Transfer learning, Hyper parameter tuning.

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