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Source: cbg-meb. Pharmaceutical Management Agency Pharmaceutical Schedule The New Zealand Pharmaceutical Schedule is a list of the prescription medicines and therapeutic products subsidised by the Government. The Pharmaceutical Schedule is updated monthly. The Clinical Practice of Drug Information. Medical classification. Categories : Pharmacological classification systems Pharmacy in Canada Identifiers Pharmacology stubs. Hidden categories: All articles with unsourced statements Articles with unsourced statements from July All stub articles.
Namespaces Article Talk. We replaced the final softmax layer with the sigmoid layer as the output is only two classes pill and background. To improve the segmentation accuracy around the pill boundaries, we increased the loss weight by two times around the pill boundaries, which is a similar technique used in U-Net. The learning rate was decreased by multiplying 0.
The network was trained until the performance stopped improving. Given the predicted masks, we apply a closing morphology operation of 10 px kernel to remove small blobs.
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For the consumer images in the dataset, the detected blobs were dilated with 10 px kernel to avoid cutoff for difficult cases. The connected components in the masks are extracted and passed to the next pipeline along with the bounding boxes. Note that we do not segment the contour using the predicted masks to avoid cutoff but to also keep the lighting context information in the background texture.
The pills are placed in a centered position and padding is inserted in order to maintain the proportion of each pill while creating a square input image.
Pill Identifier - Find Pills by Color, Shape, Imprint, or Picture
Deep CNNs have been proven successful in multiple tasks identifying object categories in pictures taken with realistic conditions of varying illumination, focus, and perspective. For this reason, we decided to employ a classification framework instead of a similarity-based one, as it was encouraged in the experimental setup of the pill recognition challenge where we got the data for these experiments.
The main goal of this work is to reliably identify pills from images under any imaging condition in order to provide accurate medication information. As such, we chose classifiers that optimize obtaining the highest accuracy rate with robustness to these challenges when users freely take pictures. To obtain the models for pill classification, we fine-tune starting from model weights pre-trained on ImageNet.
In this paper, we provide a comparison between results of applying ResNet50, SqueezeNet, 52 MobileNet, 53 and InceptionV3 54 models as the final identifier. We modify the original structure by removing the classification layers at the top, e. We connect the output of this layer with two sequential blocks, each consisting of batch-normalization, drop-out, and dense layers. The rate of these new drop-out layers is left as a hyper-parameter of each model training process. We use an Adam optimizer 51 whose initial learning rate constitutes the second hyper-parameter whose value we will find by search.
The rate is decreased to 0. The weights for each model are all learned with the same fine-tuning strategy starting from the pre-trained weights. The implementation of our pill identification service is composed of two web-based APIs that handle segmentation first and then identification.