A Combined PCA-MLP Network for Early Breast Cancer Detection
This work addresses early detection of breast cancer, a critical health issue, but is incremental as it combines existing methods (PCA and MLP) for improved accuracy.
The paper tackled early breast cancer detection by studying machine learning algorithms and proposed a combined PCA-MLP network, achieving up to 100% accuracy with a mean of 90.48% on the BCCD dataset.
Breast cancer is the second most responsible for all cancer types and has been the cause of numerous deaths over the years, especially among women. Any improvisation of the existing diagnosis system for the detection of cancer can contribute to minimizing the death ratio. Moreover, cancer detection at an early stage has recently been a prime research area in the scientific community to enhance the survival rate. Proper choice of machine learning tools can ensure early-stage prognosis with high accuracy. In this paper, we have studied different machine learning algorithms to detect whether a patient is likely to face breast cancer or not. Due to the implicit behavior of early-stage features, we have implemented a multilayer perception model with the integration of PCA and suggested it to be more viable than other detection algorithms. Our 4 layers MLP-PCA network has obtained the best accuracy of 100% with a mean of 90.48% accuracy on the BCCD dataset.