An efficient network for bacterial colony counting based on tail-MLP

This study introduces an innovative approach to microbial colony counting, leveraging the capabilities of convolutional neural networks (CNNs) and a novel Tail-MLP (Multilayer Perceptron) architecture. Traditional methods of colony counting are labor-intensive and prone to subjective errors, underscoring the need for automation. Recent advancements in deep learning offer a promising solution to these challenges. Our work presents a CNN-based model that incorporates the Tail-MLP architecture, specifically designed to enhance the accuracy and efficiency of colony enumeration across various sample types. Through a series of comparative experiments, we demonstrate the superior performance of our approach over existing methods, highlighting its fast counting speeds, high accuracy, ease of use, and excellent generalization capabilities. Additionally, we address key challenges in current counting practices, providing insights into the potential for technological improvements in microbiological research. Our contributions include a critical analysis of existing colony counting methodologies, the design and implementation of a deep learning-based model for automated colony counting, and the validation of our Tail-MLP architecture, which significantly outperforms traditional deep learning models in both accuracy and processing speed. This work not only advances the field of microbial colony counting but also offers a methodological framework for applying deep learning techniques to complex biological image processing tasks.