Intensive augmentation, cost-sensitive learning, and fine-tuning a binary model into a multi-class one lift EfficientNet B5 on BreakHis from 91.27% to 95.04% multi-class test accuracy and from 97.35% to 98.23% binary accuracy.
Analyzing histological images using hybrid techniques for early detection of multi-class breast cancer based on fusion features of cnn and hand- crafted
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Breast Tumor Classification Using EfficientNet Deep Learning Model
Intensive augmentation, cost-sensitive learning, and fine-tuning a binary model into a multi-class one lift EfficientNet B5 on BreakHis from 91.27% to 95.04% multi-class test accuracy and from 97.35% to 98.23% binary accuracy.