CoforDes: An Invariant Feature Extractor for the Drug Pill Identification

Abstract

Around 6 to 8 thousand people die annually in the world due to the fact of having taken a pill erroneously. Some works have already proposed pill recognition systems commonly using attributes related to shape, color, and others. In this work, we propose a pill feature extractor to classify them based on shape and color (CoforDes). The proposed method was compared with the descriptors GLCM, SCM, LBP, Tamura, Fourier and the Zernick, Central, Statistical and Hu Moments. Three classifiers (KNN, SVM, and Bayes) were used to evaluate the feature extractors. The attributes were extracted in 0.01006 seconds in the PILL BR dataset and 0.00810 seconds in the NIH NLM PIR dataset using CorforDes, obtaining an accuracy of 99.85% in the PILL BR dataset and 99.82% in the NIH NLM PIR dataset. The specificity was 99.82% in the PILL BR dataset and 99.91% in the NIH NLM PIR base. The results show that CoforDes is an excellent feature extractor for the extraction of drug pill images and they can be embedded in real-time applications due to their rapid processing.