About this document
ResNet-50 for Malaria Image Classification by Kevin Christian is a document available to read on EtoBox.
This conference paper discusses the application of Transfer Learning using the ResNet-50 model for classifying malaria cell images, aiming to improve diagnostic accuracy in resource-constrained areas. The study highlights the challenges faced by microscopists in accurately diagnosing malaria and presents experimental results demonstrating the effectiveness of deep learning techniques in this context. The findings indicate that Transfer Learning can yield high performance in image classification without the
- Author
- Kevin Christian
- Language
- EN