Supervisor
Matt Lemon
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This study evaluates lightweight convolutional neural networks (CNNs) for multi-class classification of microscopic fungi images in CPU-only environments, addressing resource-limited laboratories and educational settings. Four pre-trained models—EfficientNetV2-B0, MobileNetV3-Small, MobileNetV2, and NASNetMobile—were compared on the DeFungi dataset (9,114 images, five fungal classes) using stratified training, realistic data augmentation, and two-stage transfer learning. Performance was measured via accuracy, macro-F1, precision, recall, and efficiency metrics including training time, inference latency, model size, and FLOPs. EfficientNetV2-B0 achieved the highest accuracy, while MobileNetV3-Small provided the best macro-F1 and lowest computational cost, making it the optimal choice for CPU-only deployment. Findings highlight the trade-off between predictive performance and resource efficiency for microscopy image classification.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Gonzalez Donoso, C.
(2025) Comparison of Convolutional Neural Network Architectures for the Classification of Microscopic Images: Performance Evaluation of Lightweight Models. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.88