2-SiMDoM: A 2-Sieve model for detection of mitosis in multispectral breast cancer imagery
In this paper, we propose a 2-Sieve model for the detection of mitosis in breast cancer multispectral images. Multiresolution wavelet features & Gray Level Entropy Matrix (GLEM) features have been computed for each candidate on all the spectral bands. A novel dimensionality selection algorithm has been introduced and its performance compared with other existing algorithms. Data imbalance and data cleaning have been taken care of using classical data mining techniques. Furthermore, a Second Sieve classification is performed to increase the Positive Predictive Value (PPV) with minimal loss in Sensitivity. A final Sensitivity and PPV of 82.35% & 73.04% respectively was achieved over the testing set using the proposed scheme.