Muhammad Faiz AnuarSiti Munirah MohdNurhidaya Mohamad JanAnucha Watcharapasorn2025-12-022025-12-022025Muhammad Faiz Anuar, Siti Munirah Mohd, NurhidayaMohamad Jan, Anucha Watcharapasorn. (2025). Convolutional Neural Networks for Pneumonia Detection: A Preliminary Investigation and Development Framework. Citra Journal of Computer Science and Technology , 2(1), 1–15. https://doi.org/10.37934/cjcst.2.1.1153093-710810.37934/cjcst.2.1.115https://citralestari.my/index.php/cjcst/article/view/21https://oarep.usim.edu.my/handle/123456789/28087Non Indexed Publication (Tier 4)Pneumonia remains the leading infectious cause of death globally, particularly in low-resource settings where delayed diagnosis and limited radiologist availability exacerbate mortality. Existing AI-based radiograph interpretation systems often demand high computational resources and lack robustness across imaging projections. This study presents a proof-of-concept convolutional neural network diagnostic tool optimised for projection-invariant pneumonia detection under constrained conditions. Using a DenseNet-121 backbone trained on 2,000 curated images from the MIMIC-CXR dataset, our model achieved an AUC of 0.7310 and F1-score of 0.6207, with 66.36% validation accuracy. The model’s performance was consistent across posteroanterior and anteroposterior projections, though lateral view evaluation is pending. Preprocessing included CLAHE and DICOM standardisation, while augmentation improved generalisation. Though early-stage, this work shows the potential of lightweight, projection-tolerant CNNs in offline diagnosis pipelines. Future work will validate deployment feasibility on edge devices and expand evaluation across diverse patient demographics.en-USPneumoniaconvolutional neural networkmachine learningchest x-raytext::journal::journal article11521