Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification
- Title
- Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification
- Creator
- Raju N.; Augustine D.P.
- Description
- A significant role in clinical treatment and educational tasks is played by clinical image classification. However, the traditional approach has reached its peak in terms of implementation. Additionally, using traditional approaches requires a lot of time and effort to remove and choose arrangement features. The deep learning (DL) model is a new machine learning (ML) technique that has proven effective for various classification problems. To alter image classification problems, the convolutional neural network performs well, with the best results. This chapter discusses the importance and challenges of deep learning models in medical image classification and explains some techniques for reducing overfitting and leveraging model performance during model training. 2024 Taylor & Francis Group, LLC.
- Source
- Machine Intelligence: Computer Vision and Natural Language Processing, pp. 65-83.
- Date
- 2023-01-01
- Publisher
- CRC Press
- Coverage
- Raju N., Department of Computer Science, Christ University, Bangalore, India; Augustine D.P., Department of Computer Science, Christ University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-100096031-0; 978-103220199-3
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Raju N.; Augustine D.P., “Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/18406.