Seungpyo Jung

Effective liver cancer diagnosis method based on machine learning algorithm

In this paper, we introduce a method to find useful markers from sensor arrays which have massive sensing points and diagnose liver cancer based on machine learning algorithms which are neural network and fuzzy neural network. We obtain reliable results by using a learning ability and n-fold cross validation. For the verification of the proposed method, raw data of serums from 314 normal and 81 patients reacted to 1,142 aptamers are used. According to the results, we can detect liver cancer with the accuracy of 99.19 % by average use of 132 aptamers based on neural network and 98.19 % by average use of 226 aptamers based on fuzzy neural network.

The Design of Platform System for Measuring Bio-Signal with Array Sensors

Platform system for measuring the bio-signal, especially Aptamer using array sensor is designed. This platform system is based on 32-bit RISC processor, AMBA bus, peripheral device and interface for array sensors. Array sensors measure a variation of capacitance value by reaction with Aptamer from bio-signal. Processor reduces noise from measured bio-signal and carries out bio-signal processing algorithm.

Bio-signal procssor platform system for array sensors

Bio-signal processor platform system carries out the bio-signal processing extracted from array sensors. This system consists of 32-bit RISC processor, data converter circuit, array sensors and bio-signal processing algorithm. The designed specific processor includes CPU functional blocks and memory. Array sensors measure a variation of capacitance value by reaction with DNA, aptamer and protein. Processor reduces noise component from measured bio-signal and compares and detects disease by analyzing properties of bio-signals.

Design of bio-signal measurement system for array sensor and application

This paper introduces a bio-signal measuring system, which is suitable for array type bio-sensors. The system consists of 32-bits RISC processor, RAM, flash memory, UART, video controller, and ADC module. The system shows that the maximum difference of each channel capacitance without sensor loading is under 1fF. The system is used for liver cancer detection because it has an intelligence about the disease through a neural network.