Effective medical leaf identification using hybridization of GMM-CNN

Abstract

Medical plants play a vital role in curing many diseases. These plants, along with their leaves, have medicinal values. If these leaves are identified appropriately, they can be chosen directly to have more significant relief from the disease. Therefore, the identification of these medical species is a challenging task. The ecologically motivated Convolutional Neural Networks (CNNs) have substantially contributed to computer visual research. This article introduces a unique approach to medical leaf identification based on the hybridization of the Gaussian mixture model and a Convolutional Neural Network (GMM-CNN). The experimentation is performed on the Flavia dataset and is carried out using benchmark evaluation metrics. The parameters like index volume, probability of random index, and global consistency error are evaluated. The Python simulation model is utilized for the evaluation of the proposed methodology. The hybrid technique combining GMM and CNN has considerable potential in medical leaf identification. The experimental findings indicate that the hybrid strategy exhibits superior performances. The methodology suggested in this study demonstrates exceptional levels of accuracy, precision, and recall when applied to a wide range of medical leaf categories. Moreover, integrating Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN) addresses concerns associated with a scarcity of training data by offering a more resilient structure for extracting features and performing classification. By combining the advantages of statistical modelling with deep learning, we develop a resilient and precise system that has the potential to enhance botanical research, medical diagnostics, and environmental monitoring applications.