Browsing by Author "OLUSUYI, Fiyinfoluwa Ruth"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.