MatConvNet and Caffe deep learning of pattern recognition Apps on iOS devices

• Main Authors: Chao-Yuan Tien, 田兆元
• Other Authors: Jiann-Ming Wu
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/26v4v8

碩士 === 國立東華大學 === 應用數學系 === 107 === This article presents a total solution to developing artificial intelligence and pattern recognition Apps on iOS devices using MatConvNet and Caffe deep learning. The solution integrates large scale data sets, deep learning and transformation of realized Convolutional Neural Networks (CNNs) across computational platforms toward App design on iOS devices. MatConvNet deep learning on Matlab programming environments facilitates constructing pattern recognition CNNs with powerful mathematical tools and parallel and distributed processes. The iOS devices provide pattern recognition CNN Apps friendly testing environments, which have been extensively equipped with modern audio, video, and screen-touching components. The iOS Apps presented here include the published handwriting 99 multiplication, handwritten English character classification, and medical image recognition of breast cancer derived from BreakHis datasets. The pattern recognition CNNs model of each App is tested before being mounted on iOS devices. The accurate rates for model testing of the first two Apps are respectively 99.4% and 97.0%, and diagnosing lobular carcinoma breast cancer against phyllodes tumor and papillary carcinoma against adenosis attains accuracy rate of 94.9% and 87.3% respectively.