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Abstract: Medicine security is a basic issue in everyday patient consideration. Late reports show that medicine blunder is the most preventable clinical mistake. Medicine blunders address one of the main issues in medical services, with 'resemble the other the same and sound-the same' (LASA) being the lead mistake. Precisely perceiving solution pill pictures as per their visual appearance assists with guaranteeing patients' security and works with contemporary medical services framework for patients/elderly folks individuals/blind individuals. A few examination bunches have handled the pill ID issue, with arrangements because of content-based picture recovery (CBIR) and picture grouping. Notwithstanding, exact pill acknowledgment in day-to-day existence is normally thwarted by a couple of shot learning issues. Existing answers for forestall LASA have their constraints. This challenge focuses on the improvement of programming instruments to assist users with precisely recognizing known solution pills from pictures. In this venture, we propose a robotized arrangement framework for pill pictures utilizing profound learning. The profound learning calculation of a profound convolutional brain network was embraced for the execution of the proposed framework. One of the critical stages in building profound learning frameworks for pill arrangement and age is the decision of featurization for the particles. This model beat recognizable proof utilizing customary PC vision arrangements, and could help clients in distinguishing pills or medications while forestalling medicine mistakes. With a precision more prominent than 90%, the aftereffects of this undertaking might be applied to the genuine climate and may help patients to recognize pills or medications and forestall drug blunders brought about by resembling the other the same pills.