2025

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Browsing 2025 by Author "AMMARA ZAFAR"

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    Classification of blood cells and leukemia using transfer learning
    (UMT Lahore, 2025) AMMARA ZAFAR
    Classification of blood cells and detection of leukemia are critical tasks in medical diagnostics, often requiring expert knowledge and significant time. Traditional methods can be timeconsuming and susceptible to human error, underscoring the want for efficient, automated approaches or a strategy. This research delves into the detailed examination of image classification using deep learning, evaluating multiple CNN architectures to determine the most effective model. The primary objective is to improve classification accuracy while leveraging data augmentation to enhance model generalization. The research addresses key challenges, including overfitting and feature extraction efficiency, to acquire a robust predictive model. The fine-tuned VGG16 model has higher overall performance as compared to VGG19, DenseNet201, InceptionV3, and MobileNetV2, thus, it is the best model for this research. VGG16 model, after augmentation, returned a training accuracy of 95.56%, validation accuracy of 94.04%, and test accuracy of 92.81% as a predictive performance metric, outperforming all the other architectures. All of these metrics- Precision, recall, and F1-score accuracy are achieved as 0.93 and 0.93, 0.93 respectively, which indicates the model is prolifically balancing sensitivity with specificity. Using data augmentation & integration, it made test loss go from 0.1912 to 0.1824, which helps with generalization and stabilization. Specific images, their overall layout, code, and statistics illustrate the effect that data augmentation has on our classification accuracy. According to independent testing, VGG16 outperformed baseline techniques, underlining the importance of architectural selection and strategies for augmentation. For image classification tasks, the proposed VGG16 model performs best, making it a trustworthy and efficient approach.