Within the domain of medical image analysis, three distinct methodologies have demonstrated commendable accuracy: Neural Networks, Decision Trees, and Ensemble-Based Learning Algorithms, particularly in the specialized context of genstro institutional track abnormalities detection. These approaches exhibit efficacy in disease detection scenarios where a substantial volume of data is available. However, the prevalent challenge in medical image analysis pertains to limited data availability and data confidence. This paper introduces TreeNet, a novel layered decision ensemble learning methodology tailored for medical image analysis. Constructed by integrating pivotal features from neural networks, ensemble learning, and tree-based decision models, TreeNet emerges as a potent and adaptable model capable of delivering superior performance across diverse and intricate machine learning tasks. Furthermore, its interpretability and insightful decision-making process enhance its applicability in complex medical scenarios. Evaluation of the proposed approach encompasses key metrics including Accuracy, Precision, Recall, and training and evaluation time. The methodology resulted in an F1-score of up to 0.85 when using the complete training data, with an F1-score of 0.77 when utilizing 50\% of the training data. This shows a reduction of F1-score of 0.08 while in the reduction of 50\% of the training data and training time. The evaluation of the methodology resulted in the 32 Frame per Second which is usable for the realtime applications. This comprehensive assessment underscores the efficiency and usability of TreeNet in the demanding landscape of medical image analysis specially in the realtime analysis.