Gun detection and classification based on feature extraction from a new sensor array imaging system

Pneumonia is an infection caused by bacteria and viruses. It can shift from mellow to serious cases. This disease causes severe damages to the lungs since they fill with fluids. This situation causes difficulty in breathing. It further prevents oxygen to reach the blood. Pneumonia is diagnosed with the help of a chest X-rays, which can also use in the diagnosis of diseases like emphysema, lung cancer, and tuberculosis. According to WHO (World Health Organization (WHO). 2001. Standardization of Interpretation of Chest Radiographs for the Diagnosis of Pneumonia in Children. p.4.), Chest X-rays, at present, is the best available method for detecting pneumonia. Feature extraction methods like DiscreteWavelet Transform (DWT),Wavelet Frame Transform (WFT), andWavelet Packet Transform (WPT) can be used followed by any classification algorithm. In this paper, models like Squeezenet, DenseNet, and Resnet34 have been used for image recognition. In our system, the medical images were taken from Kaggle database and were recorded using a suitable imaging system. The images retrieved were then considered for input for the system where the images go through the various phases of image processing like pre-processing, edge detection and feature extraction. Later, a variety of training models are applied to know which model offers the highest accuracy.

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Design and Development of Capacitance Sensor Array for Analyzing the Non-Homogeneity in Biological Materials

Expert System Techniques in Biomedical Science Practice - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-5149-2.ch004 ◽

2018 ◽

pp. 70-96 ◽

Cited By ~ 1

Author(s):

Sahana Apparsamy ◽

Kamalanand Krishnamurthy

Keyword(s):

Sensor Array ◽

Soft Tissues ◽

Imaging System ◽

Learning Algorithm ◽

Automated Analysis ◽

Biological Materials ◽

Jaccard Index ◽

Capacitance Sensor ◽

Laser Imaging ◽

Morphological Variations

Soft tissues are non-homogeneous deformable structures having varied structural arrangements, constituents, and composition. This chapter explains the design of a capacitance sensor array for analyzing and imaging the non-homogeneity in biological materials. Further, tissue mimicking phantoms are developed using Agar-Agar and Polyacrylamide gels for testing the developed sensor. Also, the sensor employs an unsupervised learning algorithm for automated analysis of non-homogeneity. The reconstructed capacitance image can also be sensitive to topographical and morphological variations in the sample. The proposed method is further validated using a fiberoptic-based laser imaging system and the Jaccard index. In this chapter, the design of the sensor array for smart analysis of non-homogeneity along with significant results are presented in detail.

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NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

Sensors ◽

10.3390/s19143147 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3147 ◽

Cited By ~ 6

Author(s):

Liu Zhang ◽

Zhenhong Rao ◽

Haiyan Ji

Keyword(s):

Feature Extraction ◽

Hyperspectral Imaging ◽

Hyperspectral Image ◽

Imaging System ◽

Support Vector ◽

Multivariate Methods ◽

Model Combination ◽

Modeling Analysis ◽

Svm Model ◽

Wheat Kernels

In this study, a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky–Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC–NCA–SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC–NCA–SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.

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