Department of Computer and Information Sciences

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    DEVELOPMENT OF A TRANSFER LEARNING PIPELINE FOR PROSTATE CANCER AGGRESSIVENESS CLASSIFICATION
    (Covenant University Ota, 2025-08) OLUSUYI, Fiyinfoluwa Ruth; Covenant University Dissertation
    Prostate cancer is a leading malignancy in men, where accurate aggressiveness assessment is crucial for guiding treatment. While multi-parametric MRI (mpMRI) is now the established standard for non-invasive diagnosis, its interpretation can be subjective. Deep learning has shown promise, but limited data poses a challenge. This study addresses this limitation by developing a comprehensive transfer learning pipeline for automated prostate cancer aggressiveness classification using mpMRI data. The public PROSTATEx dataset was processed into 2D image patches combining T2-weighted, ADC, and high b-value DWI sequences as 3-channel inputs. Seven state-of-the-art pre-trained Convolutional Neural Network (CNN) architectures, including EfficientNet-B0, ResNet18, VGG16, DenseNet121, MobileNetV3, InceptionV3, and ShuffleNet V2, were fine-tuned using a consistent framework incorporating WeightedRandomSampler and regularization to address class imbalance. Performance evaluation was carried out on a separate validation set using a range of standard metrics, including accuracy, F1-score, specificity, and AUC. The findings identified EfficientNet-B0 as the superior architecture. It delivered the best performance, achieving an overall accuracy of nearly 97% and a macro F1-score of 0.96. This result highlights the exceptional effectiveness of modern, efficient network designs. Remarkably, the lightweight MobileNetV3 delivered nearly identical performance, also achieving a 96% accuracy and macro F1-score. Other architectures, including ShuffleNet V2, DenseNet121, and ResNet18, also proved highly effective with accuracies between 94-96%. The VGG16 and InceptionV3 models did not reach the same level of performance as the leading architectures, with accuracies of 0.72 and 0.90, respectively.
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    A MULTI-DOCUMENT SUMMARIZATION APPROACH FOR QUERY-DRIVEN NON-FACTOID QUESTION-ANSWERING SYSTEM
    (Covenant University Ota, 2025-07) EFOSA-ZUWA, Emmanuel Temidire; Covenant University Dissertation
    In Natural Language Processing (NLP), Question Answering Systems (QAS) are essential for facilitating efficient access to relevant information. Traditional QAS approaches typically involve decomposing user queries, retrieving relevant documents, and ranking potential answers, often struggle with non-factoid questions that require detailed, context-rich responses synthesized from multiple sources. While existing research has focused heavily on passage selection and ranking, many methods fail to produce a coherent answer, leaving the challenge of multi-source summarization largely unresolved. This study presents a transfer learning-based QAS framework that addresses non-factoid queries through multi-source summarization. The framework follows a multi-stage methodology incorporating question paraphrasing, contradiction detection, sentence embedding and pruning, and a hybrid approach combining extractive and abstractive summarization techniques. Quantitative and qualitative analyses were conducted using benchmark datasets, including WikiHow QA and PubMedQA to evaluate its effectiveness. The proposed system achieved strong quantitative results, with scores on WikiHow QA (ROUGE-1: 34.10, ROUGE-2: 12.30, ROUGE-L: 32.10, BLEU: 25.14, BERTScore: 95.17) and PubMedQA (ROUGE-1: 42.30, ROUGE-2: 16.10, ROUGE-L: 33.40, BLEU: 31.66, BERTScore: 95.72), demonstrating its ability to generate accurate and contextually relevant answers. Qualitative evaluations also yielded promising outcomes, with average ratings of 4.37 for information, 4.16 for conciseness, 4.20 for readability, and 4.01 for correctness on a 5-point scale, confirming the model’s effectiveness in delivering accurate and comprehensible responses. This transfer learning-based QAS framework contributes meaningfully to advancements in NLP and offers valuable support for researchers and developers working on intelligent, explainable, and practical question answering systems.