Segmentation and detection of brain tumor through optimal selection of integrated features using transfer learning

• Published in: Multimedia tools and applications Vol. 81; no. 19; pp. 27363 - 27395
• Main Authors: Swaraja, K, Meenakshi, K, Valiveti, Hima Bindu, Karuna, G
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Brain; Brain cancer; Classification; Classifiers; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Feature extraction; Image segmentation; Machine learning; Magnetic resonance imaging; Multimedia Information Systems; Particle swarm optimization; Performance measurement; Physicians; Special Purpose and Application-Based Systems; Tumors; Brain tumor; Handcrafted features; Softmax; Transfer learning; Features extraction; GoogLeNet
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12414-0

Understanding and analyzing of Magnetic resonance imaging (MRI) used in detecting the brain anamoly by specialists manually is a time-consuming, cumbersome and susceptible to intra-subject variations. Hence the proposed non-invasive Computer Aided Diagnosis (CAD) based on brain MRI is aimed to aid the radiologists and physicians to detect the presence of Glioma tumors and its variants on pulse sequences of T1, T1C, T2 and Flair. After preprocessing, using segmentation best features that differentiate one class of objects from another are selected by integrating deep learning features with handcrafted features. Later in the Classification phase, the integrated features of deep learning and handcrafted features are optimized by implementing Particle Swarm Optimization (PSO) algorithm. Finally, these integrated features are classified by Classifiers such as MSVM, KNN, ESDN and Softmax. The GoogLeNet is a pre-trained Convolution Neural Network (CNN) model employed for deep features extraction. Two popular datasets BRATS and Figshare is used for Classification of variants of Glioma tumors using a ten-fold cross-validation.The proposed system acheives high classification accuracy with MSVM Classifier when compared with Softmax, KNN and ESDA Classifiers, thus outperforming all state-of-the-art methods. The performance metrics used in this work are the Area Under Curve - Region of Operating Characteristic curve (AUC-ROC), Precision, Recall, and Specificity. Overall outcome clearly reveals that the proposed framework outperforms both the Segmentation and Classification algorithms of Brain tumors mainly in terms of computation time in contrast to the state-of-the-art methods.