A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction.

Overview

1. Department of Computer Science, Lamar University, Beaumont, TX 77705, USA; (R.S.); (B.S.); (F.S.)
2. Center for Data, AI and Cybersecurity (CDAC), Lamar University, Beaumont, TX 77705, USA
3. Center for Midstream Management and Science (CMMS), Lamar University, Beaumont, TX 77705, USA

Abstract

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and R2. The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/meharshanali — at Kaggle; found in the references
• kaggle.com/datasets/mitanshuchakrawarty — at Kaggle; found in the references

Data Availability Statement

The datasets used in this study are obtained from publicly available sources, including IEEE DataPort, TCIA, IPEDS, NSF HERD, and Kaggle, as cited in the manuscript. All preprocessing steps are described in the manuscript. Processed files used in this work are available from the corresponding author upon reasonable request. The datasets used in this study are publicly available and are used only for research and educational purposes. The authors (mainly first author) are responsible for the analysis, coding, and interpretation presented in this manuscript.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 10 keywords, 6 MeSH terms, 2 funders, 14 references.

Cite

This paper

Shrestha, R., Rana, M. M., Sun, B., Sun, F., Lou, H., & Hutson, A. (2026). A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction. Sensors (Basel, Switzerland), 26(12), 3804. https://doi.org/10.3390/s26123804

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