Detection and Identification of Pills using Machine Learning Models

IEEE BASE PAPER TITLE:

Detection and Identification of Pills using Machine Learning Models

(or)

OUR PROPOSED PROJECT TITLE:

An Accurate Deep Learning-Based Pill Detection with Intelligent Medicinal Drug Identification System

IEEE BASE PAPER ABSTRACT:

Pill color, pill size and shape are few important characteristics for automatic pill detection. However, the environmental factors may cause an effect such that a change is produced in the above factors. Often medication errors occur that may cause complications to patients and all these are caused due to damage in labels and mismatches in medicine intake, etc. In this report, a trained system is proposed using Keras and TensorFlow mainly, for easy and quick identification of varieties of pills. The detected pill (object detection) connects to the pill database wherein the pill name is detected. After the process of detection, the pre-trained dataset is used to identify the pill. Moreover, the dataset would also include the use cases and required detailed information of the respective pill. The project involves collecting datasets for automated medicine detecting technology. Effectiveness of the proposed method can be verified in the experimental results.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

MobileNet Architecture.

OUR PROPOSED ABSTRACT:

Accurate identification of medications is crucial in order to prevent medical errors, as it directly impacts the well-being of patients and avoids significant consequences. The misuse of drugs poses a serious risk to patients, leading to potential harm and complications. This issue places a burden on healthcare professionals who must manually search through pill databases to identify medications when patients are unable to provide their prescription information. This situation arises frequently as patients often discard the containers containing their medication along with the prescription.

To address these challenges, there is a pressing need to develop computerized medication systems that leverage information technology to accurately identify medications and detect potential interactions between them. This final year project introduces an innovative deep learning-based pill detection system with intelligent medicinal drug identification capabilities. The system is developed using Python programming language and utilizes the MobileNet architecture as the underlying model.

The main objective of this project is to achieve accurate pill detection from images and provide intelligent identification of medicinal drugs. To accomplish this, the system is trained on a dataset consisting of 1,268 samples for both training and testing. The system’s training process utilizes the MobileNet architecture, resulting in impressive performance metrics. The achieved training accuracy and validation accuracy are both reported at 98.00%. The high accuracy rates validate the system’s ability to effectively detect pills and identify medicinal drugs with precision.

In practice, this deep learning-based pill detection system offers significant advantages in the healthcare domain. It automates the pill identification process, reducing human error and saving valuable time for healthcare professionals. Patients can also benefit from the system, as it enables them to verify their prescribed medications and gain comprehensive information about their drugs.

The system’s evaluation includes rigorous testing on diverse pill images, ensuring its reliability, accuracy, and robustness. Through extensive experimentation and validation, the project demonstrates the effectiveness of the developed system in achieving accurate pill detection and intelligent medicinal drug identification.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 500 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 6 GB

SOFTWARE REQUIREMENTS: 

• Operating system : Windows 10 / 11.
• Coding Language : Python 3.10.9.
• Web Framework :
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

Sakthimohan. M; Elizabeth Rani. G; Amuthaguka. D; S Akshaya; Anika C Uthaman; Snigdha Sridhar, “Detection and Identification of Pills using Machine Learning Models”, 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN), IEEE Conference, 2023.

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