| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Correct filling a prescription is of paramount importance to patient’s treatment and can be critical to parent’s life. Leveraging deep learning technique’s superior performance in identifying objects seems a promising solution to assist pharmacists in filling prescriptions. However, considering the huge number of blister package types involved, common appearance similarity present, and mostly importantly scarce data quantity encountered, direct application of deep learning results in less than desirable performance. This paper proposes a highlighted deep learning (HDL) approach to address the problem. Features are highlighted before entering the learning process so that most critical features can be learned with focus. Based on an Adult Lozenge dispensing station at MacKay Memorial Hospital, a pool of 250 types of blister packages, with 54 images of each type for training and 18 images for testing, has been collected. Three classic object identification networks of YOLO v2, ResNet, and SENet, have been used to implement the proposed HDL approach, all achieving close to the perfect overall performance. Meanwhile, the proposed solution has been implemented as a prototype in an embedded system that takes into account of requirements like real-time response and cost, making the proposed HDL solution halfway to commercial products.
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