aspg-28-4499-1792223432 20261017075032 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2027 12 1 Mona Yassen Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Ancy Cheriyan School of Artificial Intelligence, Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain Pneumonia diagnosis from chest X-ray images remains clinically important because subtle radiographic abnormalities can be difficult to assess consistently under high workload or limited expert availability. This study proposes an optimization-guided diagnostic framework that integrates the Ocotillo Optimization Algorithm (OcOA) with a Convolutional Neural Network (CNN) for binary pneumonia classification. Rather than introducing OcOA or CNN as new algorithms, the contribution lies in using OcOA in two complementary stages: feature selection over CNN-derived deep representations and hyperparameter optimization of the CNN classifier. The proposed pipeline was evaluated on the Chest X-Ray Images (Pneumonia) dataset, which contains pediatric radiographs labeled as Normal or Pneumonia. In the feature-selection stage, binary Ocotillo Optimization Algorithm (bOcOA) achieved the lowest average error, with a value of 0.523417. After feature selection, the CNN baseline achieved the best classification performance among the evaluated machine-learning models, reaching an accuracy of 0.94985. In the hyperparameter optimization stage, OcOA+CNN achieved the strongest optimized performance, with an accuracy of 0.989939. These findings indicate that OcOA can improve CNN-based pneumonia detection by refining both the feature space and the model configuration. The framework may support automated chest X-ray screening on the studied dataset; however, external validation, statistical testing, and prospective clinical assessment remain necessary before any clinical deployment claim can be made. 2027 18 41 4499 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.120103 https://americaspg.com/journal/28/article/4499 https://americaspg.com/storage/articles/manuscripts/01M1YYPTET10QBY0G5HRXEWTRA.pdf