Precise classification of low quality G-banded Chromosome Images by reliability metrics and data pruning classifier
This work addresses the need for accurate karyotyping in low-budget pathological laboratories, though it is incremental as it builds on existing methods like Alex-Net and SVM.
The paper tackled the problem of classifying low-quality G-banded chromosome images in resource-limited settings by using reliability metrics and data pruning, achieving over 90% precision for chromosomes with common defects and translocations.
In the last decade, due to high resolution cameras and accurate meta-phase analyzes, the accuracy of chromosome classification has improved substantially. However, current Karyotyping systems demand large number of high quality train data to have an adequately plausible Precision per each chromosome. Such provision of high quality train data with accurate devices are not yet accomplished in some out-reached pathological laboratories. To prevent false positive detections in low-cost systems and low-quality images settings, this paper improves the classification Precision of chromosomes using proposed reliability thresholding metrics and deliberately engineered features. The proposed method has been evaluated using a variation of deep Alex-Net neural network, SVM, K Nearest-Neighbors, and their cascade pipelines to an automated filtering of semi-straight chromosome. The classification results have highly improved over 90% for the chromosomes with more common defections and translocations. Furthermore, a comparative analysis over the proposed thresholding metrics has been conducted and the best metric is bolded with its salient characteristics. The high Precision results provided for a very low-quality G-banding database verifies suitability of the proposed metrics and pruning method for Karyotyping facilities in poor countries and lowbudget pathological laboratories.