UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
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
SqueezeNet1.0 and is 2.4 times more computationally efficient than the original
model. We proposed and implemented three ultra-lightweight architecture
variants to SqueezeNet1.1 architecture, namely Variant 1 (one fire module),
Variant 2 (two fire modules), and Variant 3 (four fire modules), which are even
more compact than SqueezeNetV1.1 (eight fire modules). These models were
implemented to evaluate the best performing variant that achieves superior
computational efficiency without sacrificing accuracy in malaria blood cell
classification. The models were trained and evaluated using the NIH Malaria
dataset. We assessed each model's performance based on metrics including
accuracy, recall, precision, F1-score, and Area Under the Curve (AUC). The
results show that the SqueezeNet1.1 model achieves the highest performance
across all metrics, with a classification accuracy of 97.12%. Variant 3 (four
fire modules) offers a competitive alternative, delivering almost identical
results (accuracy 96.55%) with a 6x reduction in computational overhead
compared to SqueezeNet1.1. Variant 2 and Variant 1 perform slightly lower than
Variant 3, with Variant 2 (two fire modules) reducing computational overhead by
28x, and Variant 1 (one fire module) achieving a 54x reduction in trainable
parameters compared to SqueezeNet1.1. These findings demonstrate that our
SqueezeNet1.1 architecture variants provide a flexible approach to malaria
detection, enabling the selection of a variant that balances resource
constraints and performance.
