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3 May 2000 Neural-network diagnostic algorithm and smart sensor
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Proceedings Volume 3911, Biomedical Diagnostic, Guidance, and Surgical-Assist Systems II; (2000)
Event: BiOS 2000 The International Symposium on Biomedical Optics, 2000, San Jose, CA, United States
Digital image interpretation is the basis of medical diagnoses. Through extensive review of patient data, an algorithm was developed to identify features of diagnostic importance in radiological images. The algorithm is generally applicable. Results from cardiac, lung, and military imagery are reported. The algorithm uses a pulse coupled neural network. It is this neural network that is fabricated on a custom CMOS chip. Each neuron of the pulse coupled neural network accepts an external optical input. The optical input is accomplished by a photo-detector. The neurons communicate laterally through a voltage grid. The communication strength, light sensitivity and other global parameters are under external control. A programmable logic array is on the camera board. Data for a specific neuron is accessed by an addressing scheme typically used for a CCD array. The individual neuron speed ranges from 10 to 50 Mhertz, and is fixed by a digital clock. The current chip is configured to operate at 300 Hertz. The chip logic is a hybrid of analog and digital circuitry to minimize the neuron size, maximize the number of neurons at a fixed cost. The hybrid circuitry also minimized the noise level in the chip.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michele Ruggiero Banish, Roland B. Anderson, Heggere S. Ranganath, Brian Jones, and James C. Kirsch "Neural-network diagnostic algorithm and smart sensor", Proc. SPIE 3911, Biomedical Diagnostic, Guidance, and Surgical-Assist Systems II, (3 May 2000);

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