Achieving High Accuracy in Breast Cancer Diagnosis with CNN
DOI:
https://doi.org/10.61132/mars.v2i4.240Keywords:
convolution neural network, Breast cancer, detection, WDBC datasetAbstract
Breast cancer is a prevalent disease among women that can lead to fatalities. Using deep learning methods to detect and classify tumors can aid in the diagnostic process. Tumors can be classified as either malignant or benign, and doctors require an accurate diagnostic system to distinguish between the two. Even specialists can find it challenging to identify tumors, emphasizing the need for an automated diagnostic system for diagnosing and treating tumors. This study aims to enhance the efficiency of breast cancer diagnosis by implementing a deep convolutional neural network (DCNN). The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used in the main trials. The CNN technique utilized in this study exhibits superior performance compared to existing methods and achieves a 99.70% accuracy rate in detecting breast cancer.
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