Detection and Classification of Microcalcification Clusters in Mammograms using Evolutionary Neural Networks

Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better

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Classification of Individual and Clustered Microcalcifications in Digital Mammograms Using Evolutionary Neural Networks

Lecture Notes in Computer Science - MICAI 2006: Advances in Artificial Intelligence ◽

10.1007/11925231_115 ◽

2006 ◽

pp. 1200-1210 ◽

Cited By ~ 1

Author(s):

Rolando R. Hernández-Cisneros ◽

Hugo Terashima-Marín

Keyword(s):

Neural Networks ◽

Evolutionary Neural Networks

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Time Delay Neural Networks for the Classification of Flow Regimes

Computers & Chemical Engineering ◽

10.1016/s0098-1354(97)00076-8 ◽

1997 ◽

Vol 21 (1-2) ◽

pp. S367-S371

Author(s):

Y Yamashita

Keyword(s):

Neural Networks ◽

Time Delay ◽

Flow Regimes

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CNN-based Classification of Degraded Images

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-028 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 28-1-28-7 ◽

Cited By ~ 1

Author(s):

Kazuki Endo ◽

Masayuki Tanaka ◽

Masatoshi Okutomi

Keyword(s):

Neural Networks ◽

Image Restoration ◽

Image Classification ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Alternative Approach ◽

Degraded Image ◽

Degraded Images ◽

Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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