title: A statistical-textural-features based approach for classification of solid drugs using surface microscopic images.
abstract: The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, K-nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers.
authors.affiliation: Department of Computer Science, Lahore College for Women University, Jail Road, Lahore 54000, Pakistan.
authors.forename: Fahima
authors.identifier: 0000-0003-0215-3011
authors.initials: F
authors.lastname: Tahir
authors.affiliation: Department of Computer Science, Lahore College for Women University, Jail Road, Lahore 54000, Pakistan.
authors.forename: Muhammad Abuzar
authors.identifier: 0000-0003-4962-4546
authors.initials: MA
authors.lastname: Fahiem
mesh_terms: D001499:Bayes Theorem; D007091:Image Processing, Computer-Assisted; D008490:Medical Informatics / Q000379:methods; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D015203:Reproducibility of Results; D012984:Software; D013223:Statistics as Topic; D060388:Support Vector Machine; D013499:Surface Properties; D013696:Temperature
chemical_list: D004364:Pharmaceutical Preparations