Volume 12 • Issue 1 • PP: 18–41 • 2027
Feature Selection and Hyperparameter Optimization of Convolutional Neural Networks Using the Ocotillo Optimization Algorithm for Pneumonia Detection from Chest X-Ray Images
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© 2027 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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.
Keywords
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