IMPACTOFIMAGEPREPROCESSINGANDMODELFINE-TUNINGONCHEST X-RAY BASED PNEUMONIA CLASSIFICATION

Abstract

Despiteimprovementsinclinicalmanagement,pneumoniaisstillasignificantrespiratoryinfectionthatdemands timely radiological evaluation for early clinical intervention and minimizing preventable complications. While chest X-ray imaging is widely used as it is accessible, inexpensive, and informative for initial diagnosis, knowledgeofpatternrecognitionisnotcommonplaceduetosubtlepatternsofinfection,variationbetweenimages and there is a lack of radiological skills in resource constrained areas. In this study, the impact of image preprocessing and fine-tuning on automated pneumonia categorization on chest X-ray pictures is explored. For dichotomousclassification ofpneumonia and normal,a transfer learning framework was designed based on the models: Xception, EfficientNetB0, and DenseNet201. Our pipeline involved image resizing, model-specific normalization, data augmentation, oversampling, deep radiographic feature extraction and fine-tuned classification layers to promote generalization. The metrics used to assessperformance were based on training-validation curves, precision, accuracy, recall, F1 score and a confusion matrix. Experimental results show that Xceptionachievesthehighestaccuracyof94.23%inthetestwhencomparedtoEfficientNetB0andDenseNet201. The results show that with proper preprocessing and selective fine-tuning, it is possible to enhance the performance of computer-aided pneumonia classification for reliability and accuracy.