An Efficient andOptimized Convolution Neural Network forBrain Tumour Detection
- Title
- An Efficient andOptimized Convolution Neural Network forBrain Tumour Detection
- Creator
- Agarwal M.; Sharma L.K.; Gupta S.K.; Garg D.; Jindal M.
- Description
- Brain tumour is a life threatening disease and can affect children and adults. This study focuses on classifying MRI scan images of brain into one of 4 classes namely: glioma tumour, meningioma tumour, pituitary tumour and normal brain. Person affected with brain tumours will need treatments such as surgery, radiation therapy or chemotherapy. Pretrained Convolution Neural Networks such as VGG19, MobileNet, and AlexNet which have been widely used for image classification using transfer learning. However due to huge storage space requirements these are not effectively deployed on edge devices for creation of robotic devices. Hence a compressed version of these models have been created using Genetic Algorithm algorithm which occupies nearly 3040% of space and also a reduced inference time which is less by around 50% of original model. The accuracy provided by VGG19, AlexNet, MobileNet and Proposed CNN before compression was 92.18%, 89.45%, 93.75% and 96.85% respectively. Similarly the accuracy after compression for VGG19, AlexNet, MobileNet and Proposed CNN was 91.34%, 88.92%, 94.40% and 95.29%. 2023, Springer Nature Switzerland AG.
- Source
- Communications in Computer and Information Science, Vol-1781 CCIS, pp. 459-474.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Acceleration; Brain Tumour; CNN; Compression
- Coverage
- Agarwal M., Bennett University, Greater Noida, 201310, India; Sharma L.K., Bennett University, Greater Noida, 201310, India; Gupta S.K., Bennett University, Greater Noida, 201310, India; Garg D., Bennett University, Greater Noida, 201310, India; Jindal M., Christ Deemed to be University Delhi NCR, Mariam Nagar, Ghaziabad, 201003, India
- Rights
- Restricted Access
- Relation
- ISSN: 18650929; ISBN: 978-303135640-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Agarwal M.; Sharma L.K.; Gupta S.K.; Garg D.; Jindal M., “An Efficient andOptimized Convolution Neural Network forBrain Tumour Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19881.