Microscopic Classification of Microbial Species Using Deep Learning
- سال انتشار: 1402
- محل انتشار: دوازدهمین همایش ملی و سومین همایش بین المللی بیوانفورماتیک
- کد COI اختصاصی: IBIS12_142
- زبان مقاله: انگلیسی
- تعداد مشاهده: 115
نویسندگان
Department of Microbiology and Microbial Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
Department of Artificial Intelligence, Robotics and Cognitive Computing, Faculty of Computer Science andEngineering, Shahid Beheshti University, Tehran, Iran
Department of Microbiology and Microbial Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
چکیده
This research presents a novel approach to the differentiation of Myxococcus species fromother members of the Myxococcota phylum, leveraging the power of deep learning in microscopicimage analysis. Myxococcus, known for its complex social behavior and unique predatory lifestyle,plays a crucial role in soil ecosystems and has significant biotechnological potential. However, themorphological similarity among Myxococcota makes it challenging to distinguish between species,especially at the microscopic level [۱]. This study addresses this challenge by employing advancedimage processing techniques and fine-tuning high-performance neural networks. The methodologyinvolves a complex preprocessing pipeline to enhance the features of microscopic images ofMyxococcus species. These images are then analyzed using a customized deep learning model. Themodel, initially based on existing high-performance neural networks, is fine-tuned to suit the specifictextural and morphological characteristics of Myxococcus. This fine-tuning is critical in increasing theaccuracy and specificity of the model in differentiating Myxococcus species from closely related taxa[۲]. A significant portion of this research is dedicated to optimizing the preprocessing steps, includingnoise reduction, contrast enhancement, and feature extraction. These steps are crucial in ensuring thatthe neural network receives high-quality input data, thereby improving its learning efficiency andaccuracy. Preliminary results demonstrate that this deep learning-based approach can significantlyoutperform traditional methods in identifying and differentiating Myxococcus species. Thisadvancement holds immense potential for ecological studies and biotechnological applications, whereaccurate species identification is essential. This ongoing research is expected to contribute significantlyto the field of bioinformatics by providing a robust, efficient, and automated solution for speciesdifferentiation in microscopic images. The final outcomes of this study aim to establish a new standardin microbial image analysis, opening new avenues for exploration in microbial ecology and systematics.کلیدواژه ها
Deep Learning; Myxococcota; Image Processing; Pattern Recognitionمقالات مرتبط جدید
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