Enhancing Bacteria Classification using Image Processing and Convolutional Neural Network

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.