2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) · 2020 · 167 citations · 15 references
ComputingComputer ScienceOncologyBreast OncologyComputer VisionMachine LearningCancer DetectionCancer DiagnosisDeep LearningImage ClassificationMedical Image AnalysisTransfer LearningBreast Lesion DiagnosisBreast Tumor ClassificationClassification AccuracyClassification Accuracy AssessmentClassification Accuracy MetricsConvolutional Neural NetworksDeep Learning-based ClassificationDeep Learning-based Diagnosis
Breast cancer disease is the second most common world cause of cancer death in women. However, the early diagnostics and detection can provide a significant chance for correct treatment and survival. In this work, we propose an accurate and inclusive computational breast cancer diagnosis framework using ResNet-50 convolutional neural network to classify histopathological microscopy images. The proposed model employs transfer learning technique of the powerful ResNet-50 CNN pretrained on ImageNet to train and classify BreakHis dataset into benign or malignant. The simulation results showed that our proposed model achieves exceptional classification accuracy of 99% outperforming other compared models trained on the same dataset.
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · 2016 · 222.1K citations · Full text
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. · 2014 · 34.3K citations