Hybrid neural network and statistical classification algorithms in computer-assisted diagnosis

Rainer Stotzka

doi:10.1117/12.387751

6 June 2000 Hybrid neural network and statistical classification algorithms in computer-assisted diagnosis

Rainer Stotzka

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387751
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

The development of computer assisted diagnosis systems for image-patterns is still in the early stages compared to the powerful image and object recognition capabilities of the human eye and visual cortex. Rules have to be defined and features have to be found manually in digital images to come to an automatic classification. The extraction of discriminating features is especially in medical applications a very time consuming process. The quality of the defined features influences directly the classification success. Artificial neural networks are in principle able to solve complex recognition and classification tasks, but their computational expenses restrict their use to small images. A new improved image object classification scheme consists of neural networks as feature extractors and common statistical discrimination algorithms. Applied to the recognition of different types of tumor nuclei images this system is able to find differences which are barely discernible by human eyes.

Citation Download Citation

Rainer Stotzka "Hybrid neural network and statistical classification algorithms in computer-assisted diagnosis", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387751

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