Genetic Algorithm-Based Optimization ofUNet forBreast Cancer Classification: A Lightweight andEfficient Approach forIoT Devices
- Title
- Genetic Algorithm-Based Optimization ofUNet forBreast Cancer Classification: A Lightweight andEfficient Approach forIoT Devices
- Creator
- Agarwal M.; Dwivedi A.K.; Gupta S.K.; Najafzadeh M.; Jindal M.
- Description
- IoT devices are widely used in medical domain for detection of high blood sugar and life threatening disease such as cancer. Breast cancer is one of the most challenging type of cancer which not only affects women but in some cases men also. Deep learning is one of the widely used technology which provides efficient classification of cancerous lumps but it is not useful for IoT devices as the devices lack resources such as storage and computation. For the suitability in IoT devices, in this work, we are compressing UNet, the popular semantic segmentation technique, for the pixel-wise classification of breast cancer. For compressing the deep learning model, we use genetic algorithm which removes the unwanted layers and hidden units in the existing UNet model. We have evaluated the proposed model and compared with the existing model(s) and found that the proposed compression technique suppresses the storage requirement to 77.1%. Additionally, it also improves the inference time by 3.82without compromising the accuracy. We conclude that the primary reason of inference time improvement is the requirement of less number of weight and bias by the proposed model. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Source
- Communications in Computer and Information Science, Vol-2054 CCIS, pp. 386-396.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Compression & Acceleration; Deep Learning; Genetic Algorithm; Semantic Segmentation; UNet
- Coverage
- Agarwal M., Bennett University, Greater Noida, India; Dwivedi A.K., Bennett University, Greater Noida, India; Gupta S.K., Bennett University, Greater Noida, India; Najafzadeh M., Department of Water Engineering, Graduate University of Advanced Technology, Kerman, Iran; Jindal M., CHRIST (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 18650929; ISBN: 978-303156702-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Agarwal M.; Dwivedi A.K.; Gupta S.K.; Najafzadeh M.; Jindal M., “Genetic Algorithm-Based Optimization ofUNet forBreast Cancer Classification: A Lightweight andEfficient Approach forIoT Devices,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19487.