Histopathology based Benign/Malignant Breast Cancer Detection using Lightweight-Deep-Learning with Fused/Ensemble Features
Keywords:
Healthcare, Breast cancer, Histopathology, Deep learning, DetectionAbstract
Breast cancer (BC) is emerged as one of the harsh cancer in women which causes a large diagnostic burden globally. Early diagnosis and treatment is very essential to save the patient from the BC. Clinical level detection of the BC is commonly performed using the image supported methods which includes; (i) initial screening with a chosen traditional imaging technique, and (ii) histopathology-image (HI) supported cancer and its severity confirmation. The HI-based examination is essential to know the severity of the BC, which further supports the type of the treatment, needs to be planned and executed. The proposed study aims to develop a lightweight-deep-learning (LDL) tool to detect the benign/malignant BC using the chosen HI-data. The stages in this scheme includes; data collection and image size modification to 224x224 pixels, feature extraction with a chosen LDL-model, classification with SoftMax to identify the best model, and executing the classification with fused/ensemble deep-features to achieve better BC detection based on the chosen classifiers. This work implemented 3-fold cross validation and the outcome of this study confirms that the implemented LDL-model provided a detection accuracy of >98% with Random Forest classifier when ensemble feature is considered
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