ThoraxSense: Enhanced Thoracic Multi-DiseaseDetection on Chest X-Rays Using DenseNet121 andClass-Imbalance Optimization

Thoracic diseases are commonly detected through Chest X-rays, however class imbalance, label noise, computationalconstraints in large clinical datasets obstruct automated accurate interpretation. To address these challenges, we introduce ThoraxSense, a resource-efficient framework for multi-disease classification. In the first implementation, we utilize a PyTorch-based DenseNet121 backbone integrated with targeted GPU optimizations consisting of CUDA-aware memory management, adaptive learning-rate scheduling, and gradient clipping which in turn resulted in improved training stability on restrictedhardware. In order to achieve robust convergence and stable results, we apply dynamic class-imbalance compensationusing weighted loss functions. Further, a TensorFlow/Keras pipeline using a fine-tuned VGG16 architecture was developed inorder to evaluate cross-framework consistency. This allowed a comparative analysis across deep learning ecosystems. TheKeras-based VGG16 model achieved a mean AUROC of 0.8003 and micro AUROC of 0.8406. Focusing on reliable learning,hardware-efficient optimization, and reproducible cross-framework performance, ThoraxSense emerges as a reproducible anda practically deployable solution for thoracic disease detection, supporting real-world clinical needs.