# CNN-Based Pill Image Recognition for Retrieval Systems
## 2 Related Work  
A huge amount of work has been done by researchers over the years to improve information retrieval from stored data. The literature contains various models that significantly contributed to this area of research. In this section, we survey some of the most prominent models and approaches in the area of information retrieval by briefly going over the history and then focusing on work in pill image identification. Probabilistic information retrieval using weighted indexing was introduced in the 1960s by [PERSON] and [PERSON] [15]. The authors of [16] proposed to store and organize information using a tree-like structure, called the Adel'son-Vel'skiy and Landis (AVL) tree. [PERSON] and [PERSON] [17] improved the work done by [PERSON] [18] by proposing a picture indexing and abstraction method, which led to a paradigm shift in image retrieval. [PERSON] and [PERSON] [19; 20] proposed one of the most prominent advancements to retrieval by developing a method called System for the Mechanical Analysis and Retrieval of Text (SMART). [PERSON] and [PERSON] [21] proposed a non-text based approach for retrieving images from an extensive image database. The use of color histograms was explored by [PERSON] et al. [22]. They explained that Local Feature Regions (LFR) would be more effective in retrieving images. On the same note of color histograms, [PERSON] et al. [23] utilized [PERSON] et al. 's color histogram approach to propose an automatic pill recognition system based on pill imprint, which encompassed three features: shape, color, and texture. [PERSON] et al. extracted feature vectors based on edge localization and invariant moments of tablets as an identifier. Their experimental results showed 73% matching accuracy over a dataset of 13,000 legal drug pill images. Deep learning techniques have been introduced in Content-Based Information Retrieval (CBIR). Such techniques are used to enhance feature extraction from input images in order to identify and retrieve similar images from a database [24]. Deep learning has been impressive in its competence in recognizing objects [25; 26] and faces [27], and in handling extensive learning problems [28]. Deep learning has also improved clinical workflows by enhancing the experience of both the caregivers and patients [9]. Several deep learning-based models exist in the literature, such as the Convolutional Neural Network (CNN) [24], GoogLeNet [29], AlexNet [30], and the Residual Network (ResNet) [31]. A Convolutional Neural Network (CNN) is a deep learning technique for digital image retrieval. A CNN architecture comprises a sequence of interacting convolutional, pooling, and fully connected layers [24]. Several techniques have been developed for pill image recognition with different accuracy levels and limitations [32; 33]. MobileDeepPill [34] is a CNN architecture that integrates pill color, gradients, and shape measurements to compare between consumer and reference images. [PERSON] et al. [35] used a support vector machine (SVM) to study the color property, wherein they achieved a 97.90% overall color classification accuracy. However, the effectiveness of [PERSON] et al. 's technique is limited by certain factors such as the lighting condition, the camera resolution, and the pill and background color contrast. Some pill recognition techniques have been developed to identify a pill based on only a subset of shape, color, and imprint, such as the works in [36; 37; 38]. The work in [39] identified pills that had only one of four pre-identified colors and classes. Recently, [PERSON] et al. [40], [PERSON] et al. [41], and other similar works proposed neural network-based methods to detect pills from images. Unlike our method that aims to identify a single pill from a pill image, the approaches in [40; 42; 43] focused on identifying a pill from an image containing a group of pills. The studies in [41; 44] aimed to detect the presence of pills in an image of a blister to track oral pill intake. The work by [PERSON] et al.