Introduction
This research paper explores the use of Convolutional Neural Networks (CNNs) for pill image recognition, a critical component in medical retrieval systems. The objective was to enhance the accuracy and efficiency of identifying pills from image databases.
My Role
Co-author — This research was initially conducted as part of my Master's thesis and was later extended upon positive feedback from my professor.
Peer-Reviewed
Received on February 4, 2023, Revised on April 12, 2023, and Accepted on April 13, 2023.
Overview
Publication Date: April 18, 2023
Received: February 4, 2023
Revised: April 12, 2023
Accepted: April 13, 2023
Journal: Applied Sciences at MDPI
Institution: RIT Dubai
This research paper delves into the application of Convolutional Neural Networks (CNNs) for the purpose of pill image recognition, a crucial element in medical retrieval systems. The study aimed to improve both the accuracy and efficiency of pill identification from image databases. Initially carried out as part of my Master's thesis at RIT Dubai, the research was further extended due to encouraging feedback from my professor. The paper has been peer-reviewed and is published in the Applied Sciences journal at MDPI.
Received: February 4, 2023
Revised: April 12, 2023
Accepted: April 13, 2023
Journal: Applied Sciences at MDPI
Institution: RIT Dubai
This research paper delves into the application of Convolutional Neural Networks (CNNs) for the purpose of pill image recognition, a crucial element in medical retrieval systems. The study aimed to improve both the accuracy and efficiency of pill identification from image databases. Initially carried out as part of my Master's thesis at RIT Dubai, the research was further extended due to encouraging feedback from my professor. The paper has been peer-reviewed and is published in the Applied Sciences journal at MDPI.
CNN-BASED PILL IMAGE RECOGNITION FOR RETRIEVAL SYSTEMS
Conclusion
Continued Contribution is my aim
The research journey that culminated in the publication of this paper on Convolutional Neural Networks for pill image recognition has been both challenging and rewarding. Originating as a Master's thesis at RIT Dubai, the project grew in scope and impact, thanks in part to encouraging feedback from my professors. The subsequent publication in the esteemed journal of Applied Science at MDPI is a significant milestone, but it is not the end of the road.
The focus of this research has always been to contribute to the existing body of knowledge in the field of medical retrieval systems. The positive responses and practical applications of this research underline its significance, but they also pave the way for further exploration. As the medical and tech industries evolve, so too must our understanding and technological capabilities.
Going forward, my aim is to make continued contributions to this area of study. I plan to refine the algorithms, expand the dataset, and explore new methods for increasing accuracy and efficiency. I also intend to collaborate with other researchers and professionals in the field to push the boundaries of what is possible in medical image recognition technology.
In conclusion, the journey that began as an academic requirement has evolved into a lifelong commitment to innovation and contribution. This paper serves as both a summation of what has been achieved and a blueprint for future endeavors. I am excited to see where this path leads, and I invite the academic and professional communities to join me in this quest for continual improvement and impactful contributions.
The focus of this research has always been to contribute to the existing body of knowledge in the field of medical retrieval systems. The positive responses and practical applications of this research underline its significance, but they also pave the way for further exploration. As the medical and tech industries evolve, so too must our understanding and technological capabilities.
Going forward, my aim is to make continued contributions to this area of study. I plan to refine the algorithms, expand the dataset, and explore new methods for increasing accuracy and efficiency. I also intend to collaborate with other researchers and professionals in the field to push the boundaries of what is possible in medical image recognition technology.
In conclusion, the journey that began as an academic requirement has evolved into a lifelong commitment to innovation and contribution. This paper serves as both a summation of what has been achieved and a blueprint for future endeavors. I am excited to see where this path leads, and I invite the academic and professional communities to join me in this quest for continual improvement and impactful contributions.
