Evaluation of BI-RAD characteristics and data increment of cancer data with computer-aided diagnosis of breast cancer
Abstract
Introduction: Malignant growth is one of the main sources of death and dismalness everywhere, with 14.1 million new cases and 8.2 million passings because of disease. Early breast malignant growth location is significant for the treatment and endurance of patients. Computer-aided design is a valuable device for prior malignant growth locations.
Methodology: There are 1863 threatening and harmless cases. The 09 highlights are separated from the DDMS information base and relegated the qualities by utilizing BI-RAD mammography vocabulary. The examination is led at Radiation Oncology, AIMC/Jinnah Medical Clinic, Lahore. Case-based Reasoning (CBR) was applied at numerous information augmentation to explore its effect on the discovery of breast malignant growth. Three main distance techniques Euclidean, Manhattan, and Malik approach algorithms are used to evaluate the BI-RAD characteristics.
Results: For the Malik approach, the maximum number of correctly classified malignant cases was found by using the feature set of groups III and IV. The Manhattan distance approach provided maximum correctly classified malignant cases using feature set V and for benign cases, the group III feature set provided better results. Euclidean distance approach correctly classified the malignant cases using group V and correctly classified benign cases using groups III and II
Conclusion: It has been observed that the malignant cases are less often misclassified than the benign cases. For the malignant cases, the Malik approach produces better results and for benign cases, Manhattan distance and Euclidean distance classify better. The CBR represents a useful diagnostic tool for the classification of mammographic lesions.
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