Computer-Aided Detection of Microcalcifications in Digital Mammograms to Support Early Diagnosis of Breast Cancer

Triana, Nayid; Cerquera, Alexander

doi:10.1007/978-3-642-38637-4_30

Cited by 2 publications

(1 citation statement)

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“…It has been widely used in several remote sensing domains, such as image classification [36] and object and face detection [37], over huge datasets. In the medical field, CNN has been used for mammogram image classification and the intelligent identification of sick parts [38]. In [39], where the authors recognized handwriting through the open source network "AlexNet" and applied the methods into the smartphones, the accuracy rate reached 97.8%.…”

Section: Reflection Of Consumer Mobility Through Cnnmentioning

confidence: 99%

Sustainability Analysis and Market Demand Estimation in the Retail Industry through a Convolutional Neural Network

2018

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The Chinese retail industry is expected to grow dramatically over the next few years, owing to the rapid increase in purchasing power of Chinese consumers. Retail managers should analyze the market demands and avoid dull sales to promote the sustainable development of the retail industry. Economic sustainability in the retail industry, which refers to a suitable return of investment, requires the implementation of precise product allocation strategies in different regions. This study proposed a hybrid model to evaluate economic sustainability in the preparation of goods of retail shops on the basis of market demand evaluation. Through a grid-based convolutional neural network, a regression model was first established to model the relationship between consumer distribution and the potential market demand. Then, another model was proposed to evaluate the sustainability among regions based on their supply-demand analysis. An experiment was conducted based on the actual sales data of retail shops in Guiyang, China. Results showed an immense diversity of sustainability in the entire city and three classes of regions were distinguished, namely, high, moderate, and limited. Our model was proven to be effective in the sustainability evaluation of supply and demand in the retail industry after validation showed that its accuracy reached 92.8%.

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Section: Reflection Of Consumer Mobility Through Cnnmentioning

confidence: 99%