Detection and Identification Pills
Abstract & Details
Research Area
Computer Engineering
Keywords
Pills Detection
Deep Learning
Data Augmentation
Computer Vision
Convolutional Neural Networks
Dataset Creation
Image Recognition
Multiclass Classification
Image Segmentation
Accuracy Evaluation
deep learning
Mask R-CNN
pill detection
data augmentation
object region
object class
Abstract
Accurate pill detection and identification are vital for patient safety. Environmental factors can introduce variations in pill attributes like color, size, and shape, leading to medication errors. In this study, we propose a robust system using Keras and TensorFlow for rapid and precise pill identification. Object detection locates pills in images and links them to a comprehensive database, allowing for accurate pill name recognition. Pre-trained datasets facilitate efficient pill recognition and provide detailed information. We collect datasets for automated medicine detection, and experimental results validate the method's effectiveness.
We address the challenge of pill identification, focusing on performance improvement with limited training data. Multiple pills in a single image can lead to exponential combinations, which we tackle through innovative database expansion. Our approach outperforms existing algorithms, reducing human errors during pill inspection.
We pioneer multi-pill detection in real-world settings, introducing a multi-pill image dataset. To handle hard cases, we incorporate inter-pill relationships and achieve robust results, outperforming benchmarks. Our approach enhances patient safety through AI-based pill identification, with potential applications for optional pill use and age-based dosage adjustments.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Muthal Amruta Balasaheb | HSBPVT's FOE Kashti |
| 2 | Bodakhe Sakshi Rajesh | HSBPVT's FOE Kashti |
| 3 | Bhosale Sakshi sanjay | HSBPVT's FOE Kashti |
| 4 | Borkar Rohan Ramdas | HSBPVT's FOE Kashti |
| 5 | Sayyed Jasmine Isahak | HSBPVT's FOE Kashti |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Balasaheb, Muthal Amruta, Rajesh, Bodakhe Sakshi, sanjay, Bhosale Sakshi, Ramdas, Borkar Rohan , & Isahak, Sayyed Jasmine (2023). Detection and Identification Pills. International Journal of Advance Research and Innovative Ideas In Education, 9(6), 242-247.
MLA Style
Balasaheb, Muthal Amruta, et al. "Detection and Identification Pills." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 6, 2023, pp. 242-247.
IEEE Style
Muthal Amruta Balasaheb, Bodakhe Sakshi Rajesh, Bhosale Sakshi sanjay, Borkar Rohan Ramdas, and Sayyed Jasmine Isahak, "Detection and Identification Pills," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 6, pp. 242-247, 2023.
Vancouver Style
Balasaheb Muthal Amruta, Rajesh Bodakhe Sakshi, sanjay Bhosale Sakshi, Ramdas Borkar Rohan , Isahak Sayyed Jasmine. Detection and Identification Pills. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(6):242-247.
Harvard Style
Balasaheb, Muthal Amruta, Rajesh, Bodakhe Sakshi, sanjay, Bhosale Sakshi, Ramdas, Borkar Rohan , & Isahak, Sayyed Jasmine (2023) 'Detection and Identification Pills', International Journal of Advance Research and Innovative Ideas In Education, 9(6), pp. 242-247.
Chicago Style
Balasaheb, Muthal Amruta, et al. "Detection and Identification Pills." International Journal of Advance Research and Innovative Ideas In Education 9, no. 6 (2023): 242-247.
Turabian Style
Balasaheb, Muthal Amruta, et al. "Detection and Identification Pills." International Journal of Advance Research and Innovative Ideas In Education 9, no. 6 (2023): 242-247.
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