4,359 research outputs found
Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks
https://doi.org/10.1007/978-3-319-77712-2_62The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image
processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Potato Classification Using Deep Learning
Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in
nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest
in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and
benefit human health. They are an important staple food in many countries around the world. There are an estimated 200
varieties of potatoes, which can be classified into a number of categories based on the cooked texture and ingredient
functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify
4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating
the feasibility of this approach
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