Department of Computer Sciences

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https://repository.run.edu.ng/handle/123456789/72

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Browsing Department of Computer Sciences by Author "Toluwase A. Olowookere"

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    A Comparative Study of Two Convolutional Neural Network Models for Detecting Rice Plant Diseases Using Online and Local Image Data
    (LAUTECH Journal of Computing and Informatics (LAUJCI), 2024-03) Toluwase A. Olowookere
    Rice is one of the most widely staple foods around the globe, however, rice fields are severely affected by diseases, which can disrupt global food security. Early and accurate detection of rice diseases is essential for the recovery of such rice plants. Manually identifying rice plant diseases can be tedious and error prone. Artificial intelligence (AI) driven models, such as Convolutional Neural Networks (CNN) have proven very successful in the identification or detection of various crop diseases. This study, therefore, presents a comparative study of the effectiveness of two popular CNN architectures; ResNet and AlexNet for the detection of rice plant disease. The data used to train the models include a combination of rice leaf images that were gathered locally from a rice field/farm in Ede, Osun State, Nigeria, and from an online repository. The dataset consists of 5200 images classified into four classes: Bacterial leaf blight, Brown spot, Blast, and Healthy, each containing 1300 images. The effectiveness of the two trained models was measured using classification performance metrics including Accuracy, Precision, Recall, and F1-Score. The finding from the study showed that The ResNet has a test accuracy of 95.25% as against 92.91% for the AlexNet. The ResNet had 0.93 precision, while AlexNet recorded a precision of 0.24. For recall, the ResNet model had 0.98 while the AlexNet model had 0.23 and for the f1-score, the ResNet model had 0.95 while the AlexNet model had 0.24. Generally, the ResNet model outperformed the AlexNet model in detecting rice plant diseases, most significantly, brown spot disease.
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    DEVELOPMENT OF A PNEUMONIA DETECTION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS
    (LAUTECH Journal of Computing and Informatics, 2024-03-01) Toluwase A. Olowookere
    Pneumonia is one of the world’s most lethal and life-threatening diseases today, affecting people of all ages. Early proactive treatment can significantly reduce the likelihood of death from this illness and also prevent circumstances from worsening. Chest X-rays imaging has been one of the most well- liked and well-known clinical approaches. However, diagnosing the condition using X-rays has become increasingly challenging due to pneumonia resembling other lung disorders. As a result, this study developed a system to detect pneumonia disease in chest X-ray images using convolutional neural network-based approach. The datasets used for this study consists of 5856 chest X-ray images was obtained from Kaggle to train three pre-trained CNN architectures: VGG16, ResNet50, and MobileNetV3. The dataset was cleaned, preprocessed, and divided using the 80:20 data split ratio.Early stopping and learning rate reduction were applied to each model to prevent overfitting. The performance of each model was evaluated using accuracy, precision, recall, and f1- score on the test data. The VGG16 model outperformed others with 94% accuracy, 91% precision, 95% recall, and 93% f1-score. The MobileNetV3 model, which had the second-best performance, had an accuracy of 93%, precision of 90%, recall of 94%, and f1 score of 92%, while ResNet50 had 92% accuracy, 89% precision, 93% recall, and 91% f1-score. The best performing model of the three which is VGG16 was chosen and implemented on a web application. This system will serve as a tool for the medical practioners in detecting pneumonia earlier and accurately for proper treatment.