Abhishek D Patel
ABSTRACT
The rapid growth of the global population has brought forth challenges like pandemics, inflated medical product prices, and supply chain disruptions, leading to increased profiteering and fraud in essential medicine markets. These issues pose a grave threat to public health due to tampered drugs causing harm. Scarcity of resources, corruption in customs departments, and time constraints in inspecting pharmaceuticals worsen the problem. Testing samples in medical product manufacturing often causes delays and higher costs. To address these issues, this research aims to develop a deep learning-based solution for detecting and classifying particles in pharmaceuticals, maintain uniformity, enhancing quality control in a cost-effective and non-destructive way. The study also explores how AI can help customs departments, manufacturing units, and other companies. The research focuses on deep learning and computer vision principles, employing convolutional neural networks (CNNs) to analyse particle characteristics. The methodology involves data collection, augmentation, model selection, training, evaluation, and iterative refinement. The expected contributions include a cost-effective, non-destructive method for drug quality detection, improved efficiency in quality control, and scalability for various industries.
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Index Terms
- Drug Uniformity and Content Detection for Pharmaceutical Formulations: The Deep Learning Approach
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