Prototype Designing of Computer-Aided Classification System for leaf Images of Medicinal Plants
DOI:
https://doi.org/10.14738/jbemi.42.3053Keywords:
classification, medical leaf, feature extraction, NN classifiers, Otsu segmentation, FFBNNAbstract
The objective of the present work is to design computer-aided classification (CAC) system to discriminate among various medicinal leaves. This objective is achieved with the designing of a classification system using shape features to obtain high accuracy of end results. In order to evaluate the performance of the presented work six leaf classes were adopted and its shape features were extracted. The role of each shape feature in differentiating the leaves from one another is studied using region of convergence (ROC) curves. The combined effect of both the shape features is studied using neural network classifiers. Two neural network (NN) Classifiers were designed to classify the leaves and their performance is compared. First classifier is designed using radial bases function neural network (RBFNN) and second using feedforward backpropagation neural network (FFBPNN) with a single hidden layer. A comparative study for this dataset reveals that RBFNN shows 92% classification accuracy which is 2.7% higher than that of FFBPNN.
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