Detection of Intestinal Bleeding in Wireless Capsule Endoscopy using Machine Learning Techniques

Gastrointestinal (GI) bleeding is very common in humans, which may lead to fatal consequences. GI bleeding can usually be identified using a flexible wired endoscope. In 2001, a newer diagnostic tool, wireless capsule endoscopy (WCE) was introduced. It is a swallow-able capsule-shaped device with a camera that captures thousands of color images and wirelessly sends those back to a data recorder. After that, the physicians analyze those images in order to identify any GI abnormalities. But it takes a longer screening time which may increase the danger of the patients in emergency cases. It is therefore necessary to use a real-time detection tool to identify bleeding in the GI tract. Each material has its own spectral ‘signature’ which shows distinct characteristics in specific wavelength of light [33]. Therefore, by evaluating the optical characteristics, the presence of blood can be detected. In the study, three main hardware designs were presented: one using a two-wavelength based optical sensor and others using two six-wavelength based spectral sensors with AS7262 and AS7263 chips respectively to determine the optical characteristics of the blood and non-blood samples. The goal of the research is to develop a machine learning model to differentiate blood samples (BS) and non-blood samples (NBS) by exploring their optical properties. In this experiment, 10 levels of crystallized bovine hemoglobin solutions were used as BS and 5 food colors (red, yellow, orange, tan and pink) with different concentrations totaling 25 non-blood samples were used as NBS. These blood and non-blood samples were also combined with pig’s intestine to mimic in-vivo experimental environment. The collected samples were completely separated into training and testing data. Different spectral features are analyzed to obtain the optical information about the samples. Based on the performance on the selected most significant features of the spectral wavelengths, k-nearest neighbors algorithm (k-NN) is finally chosen for the automated bleeding detection. The proposed k-NN classifier model has been able to distinguish the BS and NBS with an accuracy of 91.54% using two wavelengths features and around 89% using three combined wavelengths features in the visible and near-infrared spectral regions. The research also indicates that it is possible to deploy tiny optical detectors to detect GI bleeding in a WCE system which could eliminate the need of time-consuming image post-processing steps

EEG biometrics based on small intra-individual and large inter-individual difference of extracted features

Biometrics refers to the process of identifying an individual from others by biological means. Most of the biometric systems are unreliable, can be imitated or even can be stolen. As a result, we need to search for a new biometrics and Electroencephalogram (EEG) based biometrics is a promising field in this aspect. By using the small intra-individual and large inter-individual difference in features with different trials, individuals can be identified with more accuracy. In this paper, a methodology for identifying an individual is proposed by determining the most effective domain and features of EEG signal. Three feed forward, back propagation multi-layer neural networks were built using the most effective features. The relative comparison shows that the network designed using the features of time domain gives the worst performance whereas the network designed using the features of both time and frequency domain gives the best performance for identifying an individual having relatively lower mean square error

Estimation of the most effective rhythm for human identification using EEG signal

Human identification using a special biological feature has become a promising field for the purpose of security system. Electroencephalogram (EEG) signals are the signature of human mind and can be used confidently as a strong biometric identifier. As EEG signals are consist of five different frequency bands, this paper represents a general methodology to determine the most effective rhythm for human identification. Using the different features from different rhythms in time and frequency domain, four neural networks are developed for the classification approach. Comparison of the designed neural networks shows that beta rhythm gives the best performance with a very low mean square error whereas delta rhythm gives the worst performance with comparative higher mean square error for identifying a person. It is concluded that beta rhythm is the most effective frequency band for human identification using EEG in resting and problem solving condition

An accurate and efficient solar tracking system using image processing and LDR sensor

The use of renewable energy resources is increasing for the purpose of producing electricity in modern world due to lack of non-renewable sources. As solar panels are becoming more popular day by day, it is a crying need to maximize the solar panel efficiency. The efficiency of solar panels can be increased to a great extent if the solar panels continuously rotate in the direction of sun. Microcontroller and an arrangement of LDR sensors can be used for the purpose of tracking the sun. But the system is less efficient because of the low sensitivity and disturbance of light dependent resistors. In this paper, a methodology of an automatic solar tracker is proposed by means of both sensors and image processing simultaneously. The mechanism of solar tracking was implemented by the use of an image processing software which combines the effect of sensors and processed image of sun and controls the solar panel accordingly. The methodology is a combination of hardware and software which can be used to control hundreds of solar panels in a solar power plant with more accuracy. It can be concluded that the proposed system is more accurate and efficient than the conventional solar tracking systems which can optimize the power requirement