Medicine Strip Identification using 2-D Cepstral Feature Extraction and Multiclass Classification Methods
This addresses a safety issue for visually impaired or general patients by improving medicine strip identification, but it is incremental as it applies existing classification methods to a specific domain.
The paper tackles the problem of misclassifying medicine strips, which can be dangerous for patients, by proposing a method using 2-D cepstral feature extraction and multiclass classification (KNN, SVM, LR) to identify them accurately, with results compared across algorithms.
Misclassification of medicine is perilous to the health of a patient, more so if the said patient is visually impaired or simply did not recognize the color, shape or type of medicine strip. This paper proposes a method for identification of medicine strips by 2-D cepstral analysis of their images followed by performing classification that has been done using the K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Logistic Regression (LR) Classifiers. The 2-D cepstral features extracted are extremely distinct to a medicine strip and consequently make identifying them exceptionally accurate. This paper also proposes the Color Gradient and Pill shape Feature (CGPF) extraction procedure and discusses the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm as well. The mentioned algorithms were implemented and their identification results have been compared.