International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
MediMitra: Advanced Pill Identification and Guidance System Using Deep Learning Archana Panpatil1, Pratiksha Deokar2, Sayali Nerkar3, Vaishnavi Nikam4, Nikita Mate5 1 Assistant Professor, Dept. of Computer Engineering, GES’s R. H. Sapat College of Engineering, Nashik,
Maharashtra, India
2,3,,4,5Student, Dept. of Computer Engineering, GES’s R. H. Sapat College of Engineering, Nashik, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------likelihood of errors, especially in settings without a Abstract - MediMitra is an innovative application that pharmacist to offer guidance.
harnesses advanced deep learning techniques to provide users with a comprehensive healthcare assistance solution. Designed to facilitate pill identification, recommend home remedies based on symptoms, and locate nearby pharmacies, the system comprises three integral modules. The Pill Identifier allows users to upload pill images for rapid analysis, matching visual features like color, shape, and imprint against a vast database, offering detailed information on each pill, including its name, uses, and precautions. The Symptoms-Based Home Remedies Recommendation module enables users to input their symptoms, where a Consultation Algorithm evaluates this data and suggests tailored, non-invasive home remedies alongside lifestyle and dietary recommendations. Finally, the Map Navigator utilizes location-based services to help users easily find nearby pharmacies for prescribed medications. By integrating artificial intelligence with healthcare, MediMitra bridges the gap between immediate health needs and professional medical guidance, empowering users to manage their health proactively and effectively.
When medication errors cause harm to a patient, they result in adverse drug events (ADEs). Importantly, ADEs associated with medication errors are preventable. Research has shown that these errors most frequently occur during the prescribing phase (56%) and the administration phase (34%) in hospital settings, where there is often no intermediary to detect or prevent the error before it reaches the patient. Factors such as the similarity in drug appearance or drug names, known as Look-Alike Sound-Alike (LASA) errors, pose a serious risk, particularly at the level of pharmacists or physicians. Such errors can put patient safety at significant risk, emphasizing the need for improved pill identification systems. While traditional methods have laid the groundwork for pill identification, recent advancements in deep learning have opened up new possibilities for improving accuracy and performance. Deep learning algorithms, particularly convolutional neural networks (CNNs), are highly effective in feature extraction and object detection tasks, making them well-suited for robust pill recognition even in uncontrolled environments [3]. Various studies have demonstrated the potential of deep learning models to identify pills from images captured on mobile devices, offering a valuable tool for both patients and healthcare professionals. However, ensuring consistent performance across diverse conditions such as varying lighting, backgrounds, and image quality remains a significant challenge.
Key Words: Pill Identification, Symptom-Based Recommendation, Deep Learning, Pharmacy Locator, Home Remedies Recommendation.
1.INTRODUCTION Medication errors are a serious issue that can have significant consequences for patient health and safety. With the vast variety of pharmaceutical products available, the accurate identification of pills has become a critical task for both healthcare professionals and patients. This task is made even more challenging by the fact that many pills share similar shapes, colors, and sizes, which makes manual identification difficult and prone to errors [1]. Medication errors can occur at multiple points, such as when a doctor prescribes the wrong drug, a pharmacist makes a dispensing error, or a patient takes medication incorrectly. Even with the best efforts of healthcare professionals, errors still happen. Problems may arise from mishandling medications, such as when an individual comes across an unknown pill or accidentally confuses two medications [2]. Issues like hardto-read labels and damaged packaging can also contribute to mistakes. Moreover, self-medication, particularly when buying drugs without professional advice, increases the
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The design, implementation, and evaluation of MediMitra, a web-based application that leverages CNNs for advanced image processing. The system is designed to automatically identify pills based on their visual characteristics, aiming to mitigate the risk of medication errors and streamline the identification process. By using CNNs, MediMitra can accurately process and analyze pill images, extracting key features such as shape, color, and size to make precise identifications. This paper evaluates the performance of MediMitra, discusses the challenges faced in its development, and highlights its potential to significantly reduce medication errors and improve patient safety.
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