Early Detection of Breast Cancer Using Forward Backpropagation Artificial Neural Network

Breast cancer can be a painful and distressing experience for both patients and their loved ones. Early detection is crucial for improving outcomes, but unfortunately, doctors may not always have the necessary resources to make an accurate diagnosis. This is where computational science can make a difference, using algorithms to aid in breast cancer detection. This study proposes an automatic classification of breast tissue using deep learning with an artificial neural network designed to detect malignant tumors at an early stage. The network has 9 neurons in the input layer, 80 in the hidden layer, and 4 in the output layer. The researchers used the “Breast Tissue” database from the Center for Machine Learning and Intelligent Systems, which includes 106 cases classified as carcinoma, fibroadenoma, mastopathy, connective tissue, and adipose tissue. However, the data required reclassification for network training and simulation, which was done using the “nntool” Matlab toolbox. The study found that the model achieved an 82% accuracy rate in tests, making it a valuable tool for health personnel in diagnosing breast cancer. Early detection significantly improves the chances of recovery, and this proposed method provides an efficient and effective way to achieve that goal.