Breast Tumor Classification by Neural Networks Fed with Sequential-Dependence Factors to the Input Layer

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概要

• 論文の詳細を見る

• We applied an artificial neural network approach to identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammograhic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extration technique has potential utility in the computer-aided diagnosis of breast cancer.

• 社団法人電子情報通信学会の論文
• 1993-08-25

著者

• FUJITA Hiroshi

  Faculty of Engineering Science, Osaka University

• Horita Katsuhei

  Department Of Diagnostic Radiology Aichi Cancer Center Hospital

• KIDO Choichiro

  Department of Diagnostic Radiology, Aichi Cancer Center Hospital

• Sakuma Sadayuki

  School Of Medicine Nagoya University

• Tsai Du-Yih

  Gifu National College of Technology

• Endo Tokiko

  School of Medicine, Nagoya University

• Tsai Du-yih

  Gifu College Of Bio-medical Technology

• Endo Tokiko

  School Of Medicine Nagoya University

• Kido Choichiro

  Department Of Diagnostic Radiology Aichi Cancer Center Hospital

• Fujita Hiroshi

  Faculty Of Engineering Gifu University

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