Enhancing Bacteria Classification using Image Processing and Convolutional Neural Network
DOI: https://doi.org/10.33003/jobasr-2023-v1i1-19
Sani Muhammed Tanko.
Muhammad Sani.
Abubakar Ahmad.
Abstract
Bacteria classification plays a vital role in the medical field, facilitating the
diagnosis and treatment of various diseases. Traditionally, clinical specialists
have relied on conventional techniques for classification, which lack predictive
capabilities. Manual classification of bacteria is a laborious and time-consuming
task that demands significant human effort. However, advancements in
technology have opened possibilities for microorganism classification through the
utilization of novel machine learning algorithms. This research explores the
integration of Convolutional Neural Networks (CNNs) for the classification of
bacterial samples, aiming to revolutionize the traditional manual classification
methods in the medical field. The methodology involves three stages: image
acquisition, feature extraction, and classification. Employing the Enhanced CNN
model, the study demonstrates the effectiveness of deep learning techniques in
image classification on a diverse bacterial species. Experimental results reveal
superior accuracy compared to existing baseline methods, showcasing the
potential of deep learning for efficient and precise bacteria classification. The
proposed approach has the potential to alleviates the manual classification burden,
saving time, and reducing dependence on human expertise. This research
contributes to advancing healthcare practices by enhancing the accuracy and
precision of 95% and 93.2% respectively for bacterial classification.
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