A concatenation of deep and texture features for medicinal trash image classification using EnSegNet-DNN-based transfer learning

Abstract

The foremost vital process in clinical trash classification is to classify the medicinal wastes into various categories like contagious, poisonous and normal wastes. For this purpose, several deep learning systems using different structures including ResNext, GoogleNet, etc., are designed. Among those systems, an Enhanced Segmentation Network (EnSegNet) with DNN-TC (EnSegNet-DNN-TC) system has achieved a higher efficiency by segmenting and classifying the trash input images. Although it segments and extracts features effectively, there are very subtle differences between many images because of their highly complex background. This leads to misjudgments of the deep learner system. Therefore in this article, an EnSegNet with Combined Feature Extraction (CFE) and DNN-TC called EnSegNet-CFE-DNN-TC system is proposed to solve misjudgments problem in deep learner classifiers due to high complex background images. First, the input images are segmented by the EnSegNet model. After that, various texture characteristics, namely Gray-Level Co-occurrence Matrix (GLCM), Multi-level Local Binary Pattern (MLBP), Local Derivative Pattern (LDP) and Local Ternary Pattern (LTP) are extracted including the deep features from the segmented images. Then, a new layer called the combination layer is introduced after the Fully Connected (FC) layers to fuse the extracted features and construct a new hybrid feature vector. This hybrid feature vector has a stronger discriminant ability compared to the single feature vector. Further, the softmax is performed to classify the medicinal wastes. Finally, the investigation outcomes reveal that the EnSegNet-CFE-DNN-TC system attains a 93.7% of classification accuracy for 100 trash images compared to the EnSegNet-DNN-TC and DNN-TC.