Advertisement
Published in:
2025 | OriginalPaper | Chapter
Comparing Training of Sparse to Classic Neural Networks for Binary Classification in Medical Data
Authors : Laura Erhan, Antonio Liotta, Lucia Cavallaro
Published in: Advances in Mobile Computing and Multimedia Intelligence
Publisher: Springer Nature Switzerland
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by (Link opens in a new window)
Abstract
Sparse Neural Networks are increasing in popularity and provide the opportunity for compact and efficient models for resource-constrained environments which are expanding as the number of IoT devices is increasing and as the Edge Computing and Fog paradigms are gaining traction. We investigate and evaluate sparsifying the training of Convolutional Neural Networks for the task of binary classification on medical datasets. We considered low (i.e., \(28\times 28\)) grey-scale resolution images that are memory-friendly and suitable for storing and analysing on lightweight devices. We found out that high sparsification strategies (above 75%) can achieve comparable performances with that of the fully connected counterpart while allowing for a reduction in inference time and peak memory usage, beneficial for resource-constrained environments part of Edge Computing. It is important to note that, as might be expected, after 90% sparsity, the performance can oscillate, and the results can vary significantly.
