# CNN-Based Pill Image Recognition for Retrieval Systems
## 1 Introduction  
Information Retrieval describes the process of sourcing information from a storage system. The retrieved information may be in the format of text, image, sound, or metadata describing a database or data. One interesting area is information retrieval from images, whereby an automated tool is used to identify objects in images. In this day and age, the increased dependency on smartphones has made informational retrieval from mobile phone photos a growing area of research [1]. Traditionally, metadata, such as keywords, captions, or image titles, have helped in information retrieval. However, this manual approach consumes time, effort, and costs. With increasing online activities, including social web applications, research on Content-Based Information Retrieval (CBIR) has become prominent in the field of Information Retrieval. CBIR is the field that describes automated image retrieval techniques that are capable of identifying images based on their \"content\", i.e., features embedded in the image, such as shape, texture, and color [2; 3; 4]. Research is still ongoing to improve the effectiveness of CBIR in terms of extracting primitive features (color and shape) and creating abstraction models to identify the level of relevance. The advances in image retrieval techniques have carved the path to applications in a variety of fields, including medicine, law enforcement, and engineering. Automated pill image recognition remains a significant application of CBIR in the field of medicine. Given the criticality of medicine consumption, there is little to no room for errors, e.g., mistakenly prescribing or taking the wrong type of medicine. Yet, there is a high possibility of errors occurring while health personnel prescribe, dispense, or administer drugs. [PERSON] and [PERSON] [5] argued that medical error ranks third on the major causes of death among hospital inpatients in the United States. WHO (the World Health Organization) statistics reveal that approximately 1.3 million patients die annually due to preventable medication blunders in the United States, which comes out to a minimum of one death per day. Moreover, WHO also admits medical error to be one of the top causes of injuries and avoidable harm [6]. Adverse Drug Effects (ADE) can also result in severe ailments, including Stevens-Johnson syndrome and Parkinson's disease [7]. WHO statistics report that health caregivers harm 4 out of every 10 patients globally [8], and [PERSON] et al. [9] report that 39% of cases are severe enough to cause injury to patients. Consumers often find it challenging to identify pills; consequently, they run into the risk of harming themselves from either consuming the wrong medication, underdosing, or overdosing. The risk of misidentifying pills is more prominent when pills are moved to different packaging containers, combined into a single container, or shared into pillboxes for ease of administration. Moreover, the financial implications of medication error are alarming: One-seventh of the Canadian budget and about 1% ($42 billion) of the total global health spending are spent on mitigating the effects of medication error [8]. To ensure safe medication consumption, each pill is made to have a distinctive appearance by having a unique combination of size, color, shape, and imprint [10]. An unidentified pill can, therefore, be cross-referenced by health practitioners against a database of prescription drugs. Pharmacists usually help their patients during a brown bag consultation with the drugs they bring in for identification. A manual search can be tedious, exhausting, and time-consuming, particularly when dealing with many pills with large generic variations. Moreover, reading tiny imprints on small drugs can easily introduce human error. Alternatively, automated pill recognition techniques can help identify pills rather quickly, decrease the possibility of pill misidentification, and provide visual assurance to the patient.