Transforming Pediatric Healthcare with Generative AI: A Hybrid CNN Approach for Pneumonia Detection

Pneumonia is one of the leading causes of morbidity and mortality in children, making its early detection critical for effective treatment. The objective of this study is to develop and evaluate a hybrid deep learning framework that combines convolutional neural networks with intuitionistic fuzzy estimators to enhance the accuracy, sensitivity, and robustness of pneumonia detection in pediatric chest X-rays. The main background is the use of intuitionistic fuzzy estimators (IFEs). The hybrid model integrates the powerful feature extraction capabilities of CNNs with the uncertainty handling and decision-making strengths of intuitionistic fuzzy logic. By incorporating an IFE, the model is better equipped to deal with ambiguity and noise in medical imaging data, resulting in more accurate and robust pneumonia detection. Experimental results on pediatric chest X-ray datasets demonstrate the effectiveness of the proposed method, achieving higher sensitivity and specificity compared to traditional CNN approaches. The hybrid system achieved a classification accuracy of 94.93%, confirming its strong diagnostic performance. In conclusion, this hybrid model offers a promising tool to assist healthcare professionals in the early and accurate diagnosis of pneumonia in children.