Supervisor
Kislay Raj
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Chest radiography (CXR) is the primary imaging tool for respiratory diseases, but interpretation can be time-consuming and requires expertise. This study compares a small custom CNN (Victorio) with three transfer-learning models (MobileNetV2, EfficientNet-B0, ResNet-50) for three-class CXR classification (normal, pneumonia, tuberculosis) and evaluates deployment on a Kubernetes cluster. Using a balanced dataset of ~13,500 CXRs, models were trained with ImageNet pre-training and from scratch. Transfer-learning models achieved near-perfect accuracy (99–100%) and AUROC, while the custom CNN reached 87–91%. Explainability analyses confirmed models focused on relevant pulmonary regions. Kubernetes deployment showed low latency, with most delays due to service overhead rather than computation. Findings indicate transfer-learning CNNs offer superior diagnostic performance without prohibitive deployment costs, supporting their practical use in clinical settings.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Anich, C.
(2025) Comparing Custom and Transfer-Learning CNN Models for Chest X-ray Classification: Evaluating Performance and Scalability with Kubernetes Orchestration. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.87