Automatic breast mass detection in mammograms using density of wavelet coefficients and a patch-based CNN

For the purpose of accurate and efficient mass detection in full-field digital mammograms, we propose a method for automated mass detection that consists of two stages: suspicious region localization and false-positive (FP) reduction, by classifying these regions into mass and non-mass regions (normal tissues).

In the first stage, the density of the wavelet coefficients based on Quincunx Lifting Scheme (DWC-QLS) is used to find suspicious regions (regions of interest, ROIs) in full mammograms. In the second stage, a patch-based CNN classifier is developed as an FP reduction to classify the suspicious regions. The main aim of this stage is to reduce the false-positive suspicious regions while keeping the true-positive suspicious regions. To further improve the performance of the FP reduction, the effectiveness of different transfer learning strategies is further explored and the best fine-tuning strategy in training InceptionV3 model is determined experimentally.

The experimental results show that the proposed method can achieve an overall performance of 0.98 TPR @1.43 FPI on the INbreast database. In addition, the suggested segmentation method detects the mass location with 100% sensitivity and average of 5.4 false positives per image.

Based on the obtained results, the introduced method was able to successfully detect and classify suspicious regions in digital mammograms and provide better TPR and FPI results in comparison with other state-of-the-art method.