Development of a Web-Based Platform Utilizing a Hybrid CNN Architecture for Real-Time Fecal Image Classification

Abstract

Accurate classification of fecal images offers a non-invasive and scalable approach to detecting enteric diseases such as Coccidiosis, Salmonellosis, and Newcastle disease. Traditional diagnostic methods remain limited by their dependence on expert analysis, time consumption, and cost. This study presents the development of a web-based platform utilizing a hybrid Convolutional Neural Network (CNN) architecture for the real-time classification of fecal images. The proposed model combines MobileNetV2 for lightweight inference and VGG-16 for deep feature extraction, ensuring both accuracy and computational efficiency. Following training on a curated and augmented dataset of labeled fecal images, the model was quantized and converted into TensorFlow Lite format for mobile compatibility. The final system enables users to upload images via a browser or Android application and receive instant classification into four categories: Coccidiosis, Salmonella, Newcastle disease, or Healthy. Performance evaluations confirm high diagnostic accuracy and responsiveness under real-world conditions. This study demonstrates that hybrid deep learning models, when integrated into web-based platforms, can support effective classification of fecal images for field-ready disease detection. It is recommended that future work expands the dataset across multiple geographic regions and incorporates additional diagnostic inputs to enhance system robustness and adaptability.