Open Access

REVIEW

A Comprehensive Review of Pill Image Recognition

Linh Nguyen Thi My^1,2,*, Viet-Tuan Le³, Tham Vo¹, Vinh Truong Hoang^3,*

1 Faculty of Information Technology, Nguyen Tat Thanh University, 300A Nguyen Tat Thanh Street, District 4, Ho Chi Minh City, 700000, Vietnam
2 Faculty of Information Technology, School of Technology, Van Lang University, 69/68 Dang Thuy Tram Street, Ward 13, Binh Thanh District, Ho Chi Minh City, 700000, Vietnam
3 Faculty of Information Technology, Ho Chi Minh City Open University, 35-37 Ho Hao Hon Street, Ward Co Giang, District 1, Ho Chi Minh City, 700000, Vietnam

* Corresponding Authors: Linh Nguyen Thi My. Email: ; Vinh Truong Hoang. Email:

(This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)

Computers, Materials & Continua 2025, 82(3), 3693-3740. https://doi.org/10.32604/cmc.2025.060793

Received 10 November 2024; Accepted 16 January 2025; Issue published 06 March 2025

Abstract

Pill image recognition is an important field in computer vision. It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety. This survey examines the current state of pill image recognition, focusing on advancements, methodologies, and the challenges that remain unresolved. It provides a comprehensive overview of traditional image processing-based, machine learning-based, deep learning-based, and hybrid-based methods, and aims to explore the ongoing difficulties in the field. We summarize and classify the methods used in each article, compare the strengths and weaknesses of traditional image processing-based, machine learning-based, deep learning-based, and hybrid-based methods, and review benchmark datasets for pill image recognition. Additionally, we compare the performance of proposed methods on popular benchmark datasets. This survey applies recent advancements, such as Transformer models and cutting-edge technologies like Augmented Reality (AR), to discuss potential research directions and conclude the review. By offering a holistic perspective, this paper aims to serve as a valuable resource for researchers and practitioners striving to advance the field of pill image recognition.

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Keywords

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