LiteChestGreenXY11n: A Lightweight Explainable YOLO Framework for Energy-Efficient Chest X-ray Abnormality Localization with Clinical Deployment Assessment and with Experimental Case Study

Technical Summary: LiteChestGreenXY11n

Problem Statement
While deep learning, particularly YOLO-based object detection, has demonstrated high performance in localizing chest X-ray abnormalities, existing models face significant barriers to clinical deployment in resource-constrained environments. Current state-of-the-art models often require high computational power, substantial energy consumption, and large memory footprints, which hinder their use in low-power medical settings. Furthermore, the "black-box" nature of deep learning limits clinical trust, as these models frequently lack explainability regarding the specific anatomical regions driving their diagnoses. Existing literature often treats detection accuracy, computational efficiency, and explainability as separate concerns, creating a gap for a unified framework that balances these factors for sustainable medical AI.

Methodology
The authors propose LiteChestGreenXY11n, a lightweight, explainable object detection framework designed for the green localization of chest X-ray abnormalities. The methodology involves the following key components:

• Architecture: The framework utilizes the YOLO11n (nano) architecture, selected for its minimal parameter count, reduced floating-point operations (FLOPs), and high inference speed. This base is adapted specifically for medical imaging to ensure efficiency without compromising detection capabilities.
• Dataset and Preprocessing: The study employs a multi-class chest X-ray dataset from Roboflow Universe, containing nine abnormality classes (e.g., pulmonary fibrosis, lung opacity, mass/nodule). The dataset comprises 2,485 training images, 1,405 validation images, and 504 test images. All images are resized to 640x640 pixels.
• Comparative Benchmarking: The proposed model is rigorously benchmarked against YOLOv5s, YOLOv8s, and YOLO11s under identical training conditions (640x640 resolution, batch size 16, 40 epochs) on NVIDIA Tesla T4 GPUs.
• Explainability (XAI): To address the interpretability gap, the framework integrates Grad-CAM (Gradient-weighted Class Activation Mapping). This technique visualizes the anatomical regions the model focuses on, providing clinicians with evidence of the decision-making process.
• Evaluation Metrics:
  • Detection Performance: Precision, Recall, mAP@50, and mAP@50–95.
  • Computational Efficiency: Parameter count, FLOPs, inference latency (ms/image), GPU memory usage, and energy consumption (Joules per inference).
  • Green AI Metrics: An Energy Efficiency Score (EES) is calculated as the ratio of mAP@50 to energy consumption.
  • Deployment Suitability: The authors introduce the Energy-Aware Clinical Deployment Suitability Index (EA-CDSI). This metric aggregates diagnostic accuracy, normalized computational complexity (GFLOPs, parameters), and energy consumption into a single score to evaluate the model's readiness for clinical deployment in resource-limited settings.

Key Results
The experimental results indicate that LiteChestGreenXY11n achieves an optimal trade-off between accuracy and efficiency:

• Detection Accuracy: LiteChestGreenXY11n achieved the highest mAP@50 of 0.355, marginally outperforming YOLOv5s (0.350), YOLOv8s (0.344), and YOLO11s (0.352). It maintained competitive precision (0.438) and recall (0.358). The text notes that while LiteChestGreenXY11n and YOLO11s have similar detection performance, the proposed model achieves this with significantly fewer resources.
• Computational Efficiency: The model demonstrated superior efficiency with 3.01 million parameters and 4.05 B FLOPs (4.046899 B), significantly lower than the baselines (e.g., YOLOv8s at 11.13M parameters and 14.23 GFLOPs). It achieved the lowest inference latency at 4.0 ms/image and the lowest computational load of 6.3 GFLOPs.
• Energy Consumption: LiteChestGreenXY11n consumed the least energy per inference at 0.588 Joules, compared to 0.965 J (YOLOv5s), 0.99 J (YOLOv8s), and 1.029 J (YOLO11s). Consequently, it attained the highest Energy Efficiency Score (EES) of 0.604 mAP/J.
• Explainability: In Grad-CAM assessments, LiteChestGreenXY11n achieved a Quality Score of 8.8 and a Localization Accuracy of 88.9%, ranking second only to YOLO11s (9.0 and 89.6%) but with substantially lower computational overhead. The heatmaps were described as "High" in clinical interpretability.
• Deployment Suitability: In the EA-CDSI analysis, LiteChestGreenXY11n received a normalized score of 1.0000, the highest among all tested models. This indicates it offers the best balance of diagnostic capability and resource efficiency, making it the most suitable candidate for deployment in low-infrastructure clinical environments.

Significance and Claims
The paper claims that LiteChestGreenXY11n successfully addresses the research gap by integrating high detection accuracy, computational efficiency, and explainability into a single framework. The authors assert that the model promotes sustainability and scalability in medical AI by significantly reducing the environmental footprint (energy and compute) without sacrificing diagnostic reliability.

The study positions LiteChestGreenXY11n as a practical solution for edge computing and resource-constrained healthcare facilities, where large-scale models are impractical. By introducing the EA-CDSI and the Clinical AI Design Space framework, the authors provide a methodological approach for evaluating the trade-offs between accuracy, cost, and energy, arguing that their proposed model represents the optimal point in this design space for current clinical needs. The work concludes that this framework enables transparent, energy-efficient, and clinically viable chest X-ray analysis, facilitating the adoption of Green AI in medical diagnostics.