MyoVision: A Mobile Research Tool and NEATBoost-Attention Ensemble Framework for Real Time Chicken Breast Myopathy Detection

2026-04-15

AI Summary

This study addresses the lack of low-cost, non-destructive, real-time detection methods for myopathies in chicken breast meat—specifically Woody Breast and Spaghetti Meat—by proposing MyoVision, a smartphone-based transmission imaging framework. The approach captures 14-bit RAW images, extracts textural features reflecting tissue microstructure, and introduces NEATBoost-Attention, a novel weighted ensemble model that combines a LightGBM classifier automatically optimized via NeuroEvolution of Augmenting Topologies (NEAT) with an attention-augmented multilayer perceptron. Evaluated on 336 commercial samples, the method achieves 82.4% classification accuracy (F1 = 0.83) across three myopathy categories, outperforming conventional machine learning and deep learning baselines and matching the performance of high-cost hyperspectral systems, while establishing a reproducible mobile RGB-D pipeline for meat quality assessment.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Ensemble Methods
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Woody Breast (WB) and Spaghetti Meat (SM) myopathies significantly impact poultry meat quality, yet current detection methods rely either on subjective manual evaluation or costly laboratory-grade imaging systems. We address the problem of low-cost, non-destructive multi-class myopathy classification using consumer smartphones. MyoVision is introduced as a mobile transillumination imaging framework in which 14-bit RAW images are captured and structural texture descriptors indicative of internal tissue abnormalities are extracted. To classify three categories (Normal, Woody Breast, Spaghetti Meat), we propose a NEATBoost-Attention Ensemble model, which is a neuroevolution-optimized weighted fusion of LightGBM and attention-based MLP models. Hyperparameters are automatically discovered using NeuroEvolution of Augmenting Topologies (NEAT), eliminating manual tuning and enabling architecture diversity for small tabular datasets. On a dataset of 336 fillets collected from a commercial processing facility, our method achieves 82.4% test accuracy (F1 = 0.83), outperforming conventional machine learning and deep learning baselines and matching performance reported by hyperspectral imaging systems costing orders of magnitude more. Beyond classification performance, MyoVision establishes a reproducible mobile RGB-D acquisition pipeline for multimodal meat quality research, demonstrating that consumer-grade imaging can support scalable internal tissue assessment.

Problem

• Woody Breast
• Spaghetti Meat
• myopathy detection
• smartphone imaging
• non-destructive classification

Innovation

• NEATBoost-Attention Ensemble
• mobile transillumination imaging
• neuroevolution optimization
• consumer-grade smartphone sensing
• myopathy classification