ML019 Deep Learning Project

Transformer-Based Electrocolonogram Signal Analysis for Automated Detection of Colonic Peristalsis Activity

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Deep Learning Architecture

Project Abstract

Applying temporal transformer structures to raw signal datasets to classify healthy and abnormal colonic movements.

Designed to address domain scalability challenges, this engineering prototype validates predictive modeling and computational analysis. It provides clean deployment scripts, structured models, and evaluation routines suitable for academic and industrial research labs.

Core System Features

  • Optimized Modeling Pipeline: Uses state-of-the-art architectures to achieve stable convergence and high-accuracy scoring.
  • Defect-Aware & Domain Adaptation Controls: Incorporates error correction routines and adaptive filtering layers to boost cross-system generalizability.
  • Practical Implementation: Developed as a model for university lab cell testing and local prototyping trials.
Web Framework PyTorch / SciPy
IDE VS Code
Database SQLite
DFD Diagram Available
Videos Available
E-R Diagram Available
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