DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI
- Title
- DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI
- Creator
- Prasad V.; Jeba Jingle I.D.; Sriramakrishnan G.V.
- Description
- A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. The input Magnetic Resonance Imaging (MRI) image is fed to the pre-processing stage. Tumour areas are segmented by utilizing SegNet in which the factors of SegNet are biased using DTDO. The image augmentation is carried out using eminent techniques, such as geometric transformation and colour space transformation. Here, features such as GIST descriptor, PCA-NGIST, statistical feature and Haralick features, SLBT feature, and CNN features are extricated. Finally, the categorization of the tumour is accomplished based on ZFNet, which is trained by utilizing DTDO. The devised DTDO is a consolidation of DTBO and CDDO. The comparison of proposed DTDO-ZFNet with the existing methods, which results in highest accuracy of 0.944, a positive predictive value (PPV) of 0.936, a true positive rate (TPR) of 0.939, a negative predictive value (NPV) of 0.937, and a minimal false-negative rate (FNR) of 0.061%. 2024 Informa UK Limited, trading as Taylor & Francis Group.
- Source
- Network: Computation in Neural Systems, Vol-35, No. 4, pp. 520-561.
- Date
- 2024-01-01
- Publisher
- Taylor and Francis Ltd.
- Subject
- A brain tumour; Child Drawing Development Optimization (CDDO); Driving Training Development Optimization (DTDO); Magnetic Resonance Imaging (MRI)
- Coverage
- Prasad V., Department of Computer Science & Engineering, Lendi Institute of Engineering & Technology, Jonnada, India; Jeba Jingle I.D., Department of Computer Science & Engineering, Christ (Deemed to be University), Bangalore, India; Sriramakrishnan G.V., Computer Science and Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
- Rights
- Restricted Access
- Relation
- ISSN: 0954898X; PubMed ID: 38801074
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Prasad V.; Jeba Jingle I.D.; Sriramakrishnan G.V., “DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/13633.