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Authors’ Response to Peer Reviews of “Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures”

Authors’ Response to Peer Reviews of “Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures”

Results detail: When mentioning model performance, briefly state why VGG16’s superior performance is significant compared to others. Response: We have revised the abstract to enhance its clarity and readability. Additionally, we included a clear Objective section to directly address the comment and make the study’s purpose more explicit. For sentence clarification, we have revised the Introduction section of the abstract to clearly indicate why current diagnostic methods are insufficient.

Alex Mirugwe, Lillian Tamale, Juwa Nyirenda

JMIRx Med 2025;6:e77221

Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures

Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures

VGG16’s superior performance is significant, as it demonstrates that a simpler model can achieve exceptional diagnostic accuracy while requiring minimal computational resources. This makes it a practical and scalable solution for deployment in resource-constrained settings with limited access to high-performance hardware. The computational time observed across models has implications for clinical settings, particularly in resource-limited environments.

Alex Mirugwe, Lillian Tamale, Juwa Nyirenda

JMIRx Med 2025;6:e66029