MS / MPhil

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Browsing MS / MPhil by Author "Mahmood Ahmad"

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    Classification of Pathogenic Bacteria Using Machine Learning and Deep Learning
    (UMT, Lahore, 2024) Mahmood Ahmad
    The present study has been designed for the classification of pathogenic bacteria species by using machine learning (ML) and deep learning (DL). The fourteen different pathogenic bacterial species included Porphyromonas gingivalis, Enterococcus faecium, Eschericia coli, Listera monocytogenes, Neisseria Gonorrhoea, Propionibacterium acnes, Clostridium perfringens, Proteus spp., streptococcus agalactiae, Staphylococcus epidermidis, Staphylococcus saprophyticus, Enterococcus faecalis, Pseudomonas aeruginosa, and Staphylococcus aureus. About of 10 thousand images of pathogenic bacteria were includedin the study with 80% training images and 20% testing images extracted from DIBaS dataset. From machine learning, Random forest, Decision tree, Naïve bayes and Support vector machinewere used, while from deep learning, VGG19, Resnet 101, Resnet, 34, Resnet 50, and Densenet 201 were used for classification purpose. For the training and testing purposes of the presented models, CNN architecture and PyTorch libraries based on Python programming language were used. All of the algorithms from machine learning and deep learningwere applied to bacteria images one by one and accuracies were recorded along with the number of iterations and average time taken by each algorithm during training and testing procedures. The results from both machine learning and deep learning architectures were then compared to find out the best method for classification purposes. In deep learning, we achieved 98.6%, 99.3%, 98.9%, 98.5%, and 98.6% accuracy produced by VGG19, Resnet 101, Resnet 34, Resnet 50 and Densenet 201 respectively. While we obtained the accuracy of 71.68%, 58.63%, 49.31%, 63.18% by using Support vector machine, Naïve bayes classifier, Decision tree, and random forest models respectively, form machine learning framework. The results depict that deep learning algorithms provided much higher accuracies than that of machine learning models. Here, deep learning architecture i.e. Resnet 101 is regarded as the best technique for automated identification of bacterial species. In addition, this is the first enhanced study on classification of pathogenic bacteria images using machine learning and deep learning.