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Deep Learning-Based Approach For Microscopic Algae Classification With Grad-CAM Interpretability by mmoklad12 is a document available to read on EtoBox.
This study presents a deep learning-based approach for classifying microscopic algae images using a convolutional neural network (CNN) integrated with squeeze and dense blocks, achieving an accuracy of 96.7%. The model outperformed traditional methods like ResNet50 and VGG16, which achieved lower accuracies of 85.0% and 93.5%, respectively. Additionally, the use of Grad-CAM enhances the model
- Author
- mmoklad12
- Language
- EN