Artificial convolution neural network for medical image pattern recognition

Lo, Shih-Chung Benedict; Chan, Heang Ping; Lin, Jyh-Shyan; Li, Huai; Freedman, Matthew T.; Mun, Seong Ki

doi:10.1016/0893-6080(95)00061-5

Cited by 267 publications

(133 citation statements)

References 16 publications

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“…Furthermore, various classification methodologies have been reported for the characterization of ROI such as, rule-based systems [9,12], fuzzy logic systems [11], statistical methods based on Markov random fields [20] and support vector machines [3]. Nevertheless, the most work reported in the literature employs neural networks for cluster characterization [10,27,33,37,51,54,55,58,59,61]. Typically, a neural network accepts as input features computed for a specific region of interest and provides as output a characterization of the region as true microcalcification cluster or not.…”

Section: Introductionmentioning

confidence: 99%

An automatic microcalcification detection system based on a hybrid neural network classifier

Papadopoulos

Fotiadis

Likas

2002

Artificial Intelligence in Medicine

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A hybrid intelligent system is presented for the identification of microcalcification clusters in digital mammograms. The proposed method is based on a three-step procedure: (a) preprocessing and segmentation, (b) regions of interest (ROI) specification, and (c) feature extraction and classification. The reduction of false positive cases is performed using an intelligent system containing two subsystems: a rule-based and a neural network sub-system. In the first step of the classification schema 22 features are automatically computed which refer either to individual microcalcifications or to groups of them. Further reduction in the number of features is achieved through principal component analysis (PCA). The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve (A z ). In particular, the A z value for the Nijmegen dataset is 0.91 and for the MIAS is 0.92. The detection specificity of the two sets is 1.80 and 1.15 false positive clusters per image, at the sensitivity level higher than 0.90, respectively. #

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Section: Introductionmentioning

confidence: 99%

An automatic microcalcification detection system based on a hybrid neural network classifier

Papadopoulos

Fotiadis

Likas

2002

Artificial Intelligence in Medicine

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“…Generally, neural networks have performed at least as well as other methods, with coronary artery disease and breast cancer among the most widely studied databases. For example, in a well publicized study, Baxt (1991) used backpropagation to identify myocardial infarction; on a coronary artery disease database, Rosenberg, Ercl, & Atlan (1993) found performance of a radial basis function network to be comparable with that of human experts and superior to various backpropagation methods; and for breast cancer detection, researchers have successfully applied backpropagation (Floyd et al 1994;, ART 2 and fractal analysis (Downes, 1994), the neocognitron (Lo et al, 1995), convolution neural networks , and decision trees (Bohren, Hadzikadic, & Hanley, 1995).…”

Section: Neural Network and Medical Diagnosismentioning

confidence: 99%

ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases

1998

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“…The CNNs develop for using advantages of the 2D structured input image or speech signals [22]. This is attained with local connections and tied weights followed by some form of pooling which results in translation constant features.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Stacked Autoencoder Method for Fabric Defect Detection

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Yüksek

2017

Cumhuriyet Science Journal

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Abstract. The fabric defect detection has crucial importance in terms of sectoral quality. As fabric defection stage, accordingly the growing market volume and production capacity, detection via human vision has caused largely time-wasting and success rate decreasing until 60%. Due to a fabric has unique texture, there is necessity for it to work on separately from other images types while extracting its features. Features are vital material of computer vision especially classification problems. Hence, extracting right features is the most significant stage of error detection. This purpose in mind on this study, deep learning which distinguishes with multi-layer architectures and reveals high achievement on image and speech procession recent years by self-feature extraction is applied to fabric defect detection. Stacked autoencoder -a deep learning method-that aimed to represent input data via compression or decompression is tried to detect defect of fabrics and it gained acceptable success. The principal aim of this study is to increase achievement of feature extraction by tuning up the input value and hyper parameters autoencoder. Thanks to the fine tuning of hyper-parameters of deep model, we have 96% success rate on our own dataset. Keywords: Deep learning, fabric defect detection, autoencoder, hyper parameter Kumaş Hatası Tespiti için Yığınlanmış Oto-kodlayıcı YöntemiÖzet. Kumaş hatası tespiti sektörel kalite açısından önem arz etmektedir. Bu hataların tespitinde, gelişen pazar hacmi ve üretim kapasitelerinin büyüklüğü sebebiyle insan görüsü ile tespit, büyük oranda zaman kaybına ve hata tespit oranının %60 seviyelerine kadar düşmesine sebep olmaktadır. Bu bağlamda daha yüksek başarım elde edebilmek için görüntü işleme alanında bir çok yöntem denenmiştir. Kumaşın kendine has bir dokusunun olması sebebiyle, öznitelikleri çıkarılırken diğer görüntü türlerinden ayrı olarak incelenmesi gerektirmektedir. Öznitelikler bilgisayarlı görmede özellikle sınıflandırma problemlerinde ham madde olmaktadır. Bu yüzden doğru öznitelikleri çıkarmak, hata tespitinde en önemli aşamadır. Bu amaç doğrultusunda, çoklu-katman mimarisi ve kendi özniteliklerini çıkararak son yıllarda görüntü ve ses işleme alanında büyük başarılar getirmesi ile öne çıkan derin öğrenme kumaş hatası tespitine uygulanmıştır. Giriş verisini sıkıştırma ya da genişletme ile temsil eden yığınlı oto-kodlayıcılar -bir derin öğrenme yöntemi-kumaş hatası tespitinde denenmiş ve kabul edilebilir başarılar elde edilmiştir. Çalışmanın asıl amacı oto kodlayıcının hiper parametreleri ve giriş değeri ile oynamalar yaparak öznitelik çıkarımı başarısını artırmaktır. Derin modelin hiper parametrelerin ince ayarıyla, kendi veri setimizde %96'lık bir başarı oranı elde ettik.Anahtar Kelimeler: Derin öğrenme, kumaş hatası tespiti, oto-kodlayıcı, hiper parametre

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Artificial convolution neural network for medical image pattern recognition

Cited by 267 publications

References 16 publications

An automatic microcalcification detection system based on a hybrid neural network classifier

An automatic microcalcification detection system based on a hybrid neural network classifier

ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases

Stacked Autoencoder Method for Fabric Defect Detection

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