calib mopso a calibration aware multi objective feature selection framework for pneumonia detection from chest x ray images

Pneumonia is a leading global health concern, and chest x-ray imaging is widely used for its diagnosis. manual interpretation of radiographs is time-consuming and subject to variability, motivating the need for automated and reliable computer-aided systems. we propose calib-mopso, a calibration-aware multi-objective feature selection framework for pneumonia detection. deep features are extracted using a fine-tuned resnet-50 with a 512-dimensional projection head, and feature subsets are optimized through a multi-objective particle swarm optimization procedure. the framework jointly maximizes discrimination (average precision), promotes sparsity, improves probability calibration (brier score). selected features are classified with a probability-calibrated support vector machine using a radial basis function kernel. experiments on the benchmark chest x-ray dataset show that calib-mopso reduces features by nearly ninety-four percent while achieving strong discrimination (area under the roc curve of 0.998 and area under the precision-recall curve of 0.999). on the held-out test set, the method maintained high recall and generalized well to malaria and covid-19 datasets. overall, calib-mopso yields compact, calibrated, and generalizable models suitable for medical imaging tasks.