Comput Math Methods Med - A statistical-textural-features based approach for classification of solid drugs using surface microscopic images.

Tópicos

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Resumo

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 na?ve 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.

Resumo Limpo

qualiti pharmaceut product play import role pharmaceut industri well live usag defect tablet can harm patient research propos nondestruct method identifi defect nondefect tablet use surfac morpholog three differ environment factor temperatur humid moistur analyz evalu perform propos method multipl textur featur extract surfac defect nondefect tablet textur featur gray level cooccurr matrix run length matrix histogram autoregress model haar wavelet total textur featur extract imag perform analysi top top featur top featur extract use three differ featur reduct techniqu chisquar gain ratio relieff research use three differ classifi support vector machin knearest neighbor nave bay calcul accuraci propos method use two experi leaveoneout crossvalid techniqu train test model test classifi select featur perform comparison result experiment work result case svm perform better two classifi

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