UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices

Lightweight deep learning approaches for malaria detection have gained attention for their potential to enhance diagnostics in resource constrained environments. For our study, we selected SqueezeNet1.1 as it is one of the most popular lightweight architectures. SqueezeNet1.1 is a later version of S...

Full description

Saved in:
Bibliographic Details
Main Authors: Suresh Babu Nettur, Shanthi Karpurapu, Unnati Nettur, Likhit Sagar Gajja, Sravanthy Myneni, Akhil Dusi, Lalithya Posham
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Malaria NIH dataset
lightweight
compact
SqueezeNet
fire module
AlexNet
Online Access:https://ieeexplore.ieee.org/document/11007680/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/11007680/

Similar Items