# TreeNet: Layered Decision Ensembles

arXiv: https://arxiv.org/abs/2510.09654 · HTML: https://mlsanity.com/paper/2510.09654/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 120 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Conflict-Averse Gradient Descent for Multi-task Learning](https://mlsanity.com/paper/2110.14048/) (2021)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks](https://mlsanity.com/paper/1810.02281/) (2018)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Lung and Colon Cancer Histopathological Image Dataset (LC25000)](https://mlsanity.com/paper/1912.12142/) (2019)
- [A Unified Theory of Diversity in Ensemble Learning](https://mlsanity.com/paper/2301.03962/) (2023)
- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Diversity and Generalization in Neural Network Ensembles](https://mlsanity.com/paper/2110.13786/) (2021)
- [Convergence of gradient descent for deep neural networks](https://mlsanity.com/paper/2203.16462/) (2022)
- [Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective](https://mlsanity.com/paper/2306.16893/) (2023)
- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning](https://mlsanity.com/paper/2405.16879/) (2024)
- [Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2103.08212/) (2021)
- [A Comparative Study on Polyp Classification using Convolutional Neural Networks](https://mlsanity.com/paper/2007.06071/) (2020)
- [Neural Complexity Measures](https://mlsanity.com/paper/2008.02953/) (2020)
- [Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization](https://mlsanity.com/paper/1611.02120/) (2016)
- [Convolutional neural networks for medical image segmentation](https://mlsanity.com/paper/2211.09562/) (2022)
- [Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks](https://mlsanity.com/paper/2303.17925/) (2023)
- [Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges](https://mlsanity.com/paper/2307.16262/) (2023)

## Abstract (arXiv)

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.