Recognition of Ellipsoid-like Herbaceous Tibetan Medicinal Materials Using DenseNet with Attention and ILBP-Encoded Gabor Features

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 6; p. 847
• Main Authors: Zhou, Liyuan, Gao, Hongmei, Gao, Dingguo, Zhao, Qijun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25-05-2023; MDPI
• Subjects: Accuracy; Algorithms; Clutter; Color; Data augmentation; Datasets; Errors; Experiments; Feature extraction; Herbal medicine; Identification methods; image recognition; local binary patterns; Machine learning; Medical imaging; Medical research; Medicine; Methods; multi-feature fusion; Museums; Texture recognition; Tibetan medicinal materials; Wavelet transforms; China; Tibetan medicinal materials; local binary patterns; multi-feature fusion; image recognition
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25060847

Tibetan medicinal materials play a significant role in Tibetan culture. However, some types of Tibetan medicinal materials share similar shapes and colors, but possess different medicinal properties and functions. The incorrect use of such medicinal materials may lead to poisoning, delayed treatment, and potentially severe consequences for patients. Historically, the identification of ellipsoid-like herbaceous Tibetan medicinal materials has relied on manual identification methods, including observation, touching, tasting, and nasal smell, which heavily rely on the technicians' accumulated experience and are prone to errors. In this paper, we propose an image-recognition method for ellipsoid-like herbaceous Tibetan medicinal materials that combines texture feature extraction and a deep-learning network. We created an image dataset consisting of 3200 images of 18 types of ellipsoid-like Tibetan medicinal materials. Due to the complex background and high similarity in the shape and color of the ellipsoid-like herbaceous Tibetan medicinal materials in the images, we conducted a multi-feature fusion experiment on the shape, color, and texture features of these materials. To leverage the importance of texture features, we utilized an improved LBP (local binary pattern) algorithm to encode the texture features extracted by the Gabor algorithm. We inputted the final features into the DenseNet network to recognize the images of the ellipsoid-like herbaceous Tibetan medicinal materials. Our approach focuses on extracting important texture information while ignoring irrelevant information such as background clutter to eliminate interference and improve recognition performance. The experimental results show that our proposed method achieved a recognition accuracy of 93.67% on the original dataset and 95.11% on the augmented dataset. In conclusion, our proposed method could aid in the identification and authentication of ellipsoid-like herbaceous Tibetan medicinal materials, reducing errors and ensuring the safe use of Tibetan medicinal materials in healthcare.