Develop a back-propagation neural network for diagnosing breast cancer based on features computed from a digitised image of a fine needle aspirate (FNA) of a breast mass. The data set contains 150 cases. Each case is diagnosed as malignant or benign based on 10 real-valued features: radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry and fractal dimension. The data set is located on the book’s website: http://www.booksites.net/negnevitsky.

Explain the difficulties and limitations of your diagnostic neural network.