Design a Model to Analyze Open Source Nodejs IoT Frameworks
Keywords:
Leukemia Detection, CNN, Deep Learning, Blood Smear Images, EfficientNet-B0, Image Classification, Medical Imaging, Performance Evaluation.Abstract
Leukemia is a life-threatening blood cancer that requires early and accurate diagnosis for effective treatment. Traditional diagnostic methods are time-consuming and subject to human error. In this study, we evaluate the performance of multiple Convolutional Neural Network (CNN) architectures including VGG-16, VGG-19, ResNet-50, ResNet-152, Xception, EfficientNet-B0, and a Proposed EfficientNet-B0 for the detection and classification of leukemia using microscopic blood smear images. The models were trained and tested on a public dataset to classify images as Healthy or Cancer. Performance was assessed using Accuracy, Precision, Recall, and F1-Score. The Proposed EfficientNet-B0 model outperformed all baseline architectures, achieving 98% across all evaluation metrics, marking a significant improvement of +2.78% in accuracy compared to the best-performing standard model.Downloads
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