scholarly journals A Multi-layer Perceptron Approach to Automatically Detect Tissue via NIR Multispectral Imaging

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Sandeep Gurm ◽  
Ossama Badawy ◽  
Alexander Wong
Keyword(s):  
Sensitivity And Specificity ◽  
Multispectral Imaging ◽  
Human Subjects ◽  
Isosbestic Point ◽  
Multi Layer Perceptron ◽  
Multispectral Data ◽  
Model Yield

We present a novel pixel-level spectra based multi-layer perceptron(MLP) to discriminate regions of biomedical multispectral imagingdata into two categories: tissue and non-tissue. The spectra usedfor this study are 740nm, 780nm, 850nm, and 945nm as thesewavelengths are on either side of the isosbestic point for oxyhemoglobinand deoxyhemoglobin; absorbers that are common in allhealthy tissues. An MLP is trained using multispectral data from12 human subjects and 12 non-tissue objects. The MLP is testedon three multispectral challenge image sets, from which the accuracy,sensitivity, and specificity of the model yield results of 91.3%(+/-0.2%), 98.1% (+/-0.3%), and 88.5% (+/- 0.3%) respectively.

scholarly journals Multispectral Image under Tissue Classification Algorithm in Screening of Cervical Cancer

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Pei Wang ◽  
Shuwei Wang ◽  
Yuan Zhang ◽  
Xiaoyan Duan
Keyword(s):  
Cervical Cancer ◽  
Clinical Application ◽  
Sensitivity And Specificity ◽  
Statistical Difference ◽  
Multispectral Imaging ◽  
Classification Algorithm ◽  
Multispectral Image ◽  
Multispectral Images ◽  
Tissue Classification ◽  
The Difference

The objectives of this study were to improve the efficiency and accuracy of early clinical diagnosis of cervical cancer and to explore the application of tissue classification algorithm combined with multispectral imaging in screening of cervical cancer. 50 patients with suspected cervical cancer were selected. Firstly, the multispectral imaging technology was used to collect the multispectral images of the cervical tissues of 50 patients under the conventional white light waveband, the narrowband green light waveband, and the narrowband blue light waveband. Secondly, the collected multispectral images were fused, and then the tissue classification algorithm was used to segment the diseased area according to the difference between the cervical tissues without lesions and the cervical tissues with lesions. The difference in the contrast and other characteristics of the multiband spectrum fusion image would segment the diseased area, which was compared with the results of the disease examination. The average gradient, standard deviation (SD), and image entropy were adopted to evaluate the image quality, and the sensitivity and specificity were selected to evaluate the clinical application value of discussed method. The fused spectral image was compared with the image without lesions, it was found that there was a clear difference, and the fused multispectral image showed a contrast of 0.7549, which was also higher than that before fusion (0.4716), showing statistical difference ( P < 0.05 ). The average gradient, SD, and image entropy of the multispectral image assisted by the tissue classification algorithm were 2.0765, 65.2579, and 4.974, respectively, showing statistical difference ( P < 0.05 ). Compared with the three reported indicators, the values of the algorithm in this study were higher. The sensitivity and specificity of the multispectral image with the tissue classification algorithm were 85.3% and 70.8%, respectively, which were both greater than those of the image without the algorithm. It showed that the multispectral image assisted by tissue classification algorithm can effectively screen the cervical cancer and can quickly, efficiently, and safely segment the cervical tissue from the lesion area and the nonlesion area. The segmentation result was the same as that of the doctor's disease examination, indicating that it showed high clinical application value. This provided an effective reference for the clinical application of multispectral imaging technology assisted by tissue classification algorithm in the early screening and diagnosis of cervical cancer.

scholarly journals Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing

2020 ◽  
Vol 12 (14) ◽  
pp. 2194
Author(s):  
Francesco Savian ◽  
Marta Martini ◽  
Paolo Ermacora ◽  
Stefan Paulus ◽  
Anne-Katrin Mahlein
Keyword(s):  
Remote Sensing ◽  
Multispectral Imaging ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
First Record ◽  
Temperature Data ◽  
Expert Assessment ◽  
Plant Vigour ◽  
Multispectral Data ◽  
Thermal Data

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.

scholarly journals Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Quoc T. Huynh ◽  
Uyen D. Nguyen ◽  
Lucia B. Irazabal ◽  
Nazanin Ghassemian ◽  
Binh Q. Tran
Keyword(s):  
Sensitivity And Specificity ◽  
Human Subjects ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Motion Data ◽  
Time Motion ◽  
High Acceleration ◽  
The Usa ◽  
Velocity Changes

Falling is a common and significant cause of injury in elderly adults (>65 yrs old), often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS) comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling). Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.

scholarly journals Detecting Myocardial Infarction by Electrocardiogram Machine Learning Models With Greater Accuracy; A Technical Advance Article

2021 ◽  
Author(s):  
M.D.S. Sudaraka ◽  
I. Abeyagunawardena ◽  
E. S. De Silva ◽  
S Abeyagunawardena
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Random Forest ◽  
Decision Trees ◽  
Sensitivity And Specificity ◽  
Random Forest Model ◽  
Multi Layer Perceptron ◽  
Learning Models ◽  
Forest Model ◽  
Machine Learning Models

Abstract BackgroundElectrocardiogram (ECG) is a key diagnostic test in cardiac investigation. Interpretation of ECG is based on the understanding of normal electrical patterns produced by the heart and alterations of those patterns in specific disease conditions. With machine learning techniques, it is possible to interpret ECGs with increased accuracy. However, there is a lacuna in machine learning models to detect myocardial infarction (MI) coupled with the affected territories of the heart. MethodsThe dataset was obtained from the University of California, Irvine, Machine Learning Repository. It was filtered to obtain observations categorized as Normal, Ischemic changes, Old Anterior MI and Old Inferior MI. The dataset was randomly split into a training set (70%) and a test set (30%). 73 out of the 270 ECG features were selected based on the changes observed following MI, after excluding predictors that had near zero variance across the observations. Three machine learning classification models (Bootstrap Aggregation Decision Trees, Random Forest, Multi-layer Perceptron) were trained using the training dataset, optimizing for the Kappa statistic and the parameter tuning was achieved with repeated 10-fold cross validation. Accuracy and Kappa of the samples were used to evaluate performance between the models. ResultsThe Random Forest model identified old anterior and old inferior MIs with 100% sensitivity and specificity and all 4 categorized observations with an overall accuracy of 0.9167 (95% CI 0.8424 - 0.9633). Both the Bootstrap Aggregation Decision Trees and the Multi-layer Perceptron models identified old anterior MIs with 100% sensitivity and specificity and their overall accuracies for all 4 observations were 0.8958 (95% CI 0.8168 - 0.9489) and 0.8542 (95% CI 0.7674 - 0.9179) respectively.Conclusion With a medically informed feature selection we were able to identify old anterior MI with 100% sensitivity and specificity by all three models in this study, and old inferior MI with 100% sensitivity and specificity by Random Forest Model. If the data set can be improved it is possible to utilize these machine learning models in hospital setting to identify cardiac emergencies by incorporating them into cardiac monitors, until trained personnel become available.

scholarly journals Bombs and cocaine: detecting nefarious nitrogen sources using remote sensing and machine learning

2019 ◽  
Author(s):  
Christopher Adams ◽  
Soo Mei Chee ◽  
David Bell ◽  
Oliver P.F. Windram
Keyword(s):  
Machine Learning ◽  
Ammonium Nitrate ◽  
Multispectral Imaging ◽  
Nitrogen Sources ◽  
Growth And Yield ◽  
Calcium Ammonium Nitrate ◽  
Multispectral Data ◽  
Feeding Regimes ◽  
Illegal Activities ◽  
Hyperspectral Sensing

AbstractPlants are treated with synthetic or organic nitrogen sources to increase growth and yield, the most common being calcium ammonium nitrate. However, some nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive manufacture and ammonium sulphate in the cultivation and extraction of the narcotic cocaine from Erythroxylum spp. Here we show that hyperspectral sensing, multispectral imaging and machine learning image analysis can be used to visualise and differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis of leaves from plants exposed to different nitrogen sources reveals shifts in colourful metabolites that may contribute to altered reflectance signatures. Overall this suggests that different nitrogen feeding regimes alter plant secondary metabolism leading to changes in the reflectance spectrum detectable via machine learning of multispectral data but not the naked eye. Our results could facilitate the development of technologies to monitor illegal activities involving various nitrogen sources and further inform nitrogen application requirements in agriculture.

scholarly journals Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Nele Bendel ◽  
Anna Kicherer ◽  
Andreas Backhaus ◽  
Hans-Christian Klück ◽  
Udo Seiffert ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Original Data ◽  
Economic Losses ◽  
Disease Detection ◽  
Field Detection ◽  
Multispectral Data ◽  
True Incidence ◽  
Grapevine Trunk Diseases

Abstract Background Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging. Results Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult. Conclusions In this study, ground-based hyperspectral and airborne multispectral approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.

Sensitivity and specificity of multispectral imaging in detecting central serous chorioretinopathy

2016 ◽  
Vol 49 (5) ◽  
pp. 498-505 ◽  
Author(s):  
Xuemei Zhu ◽  
Yong Cheng ◽  
Xue Pan ◽  
Enzhong Jin ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Central Serous Chorioretinopathy ◽  
Multispectral Imaging

Is more data better? A comparison of multi- and hyperspectral imaging in phenotyping.

2020 ◽  
Author(s):  
Marja Haagsma ◽  
Gerald Page ◽  
Jeremy Johnson ◽  
Christopher Still ◽  
Kristen Waring ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Multispectral Imaging ◽  
Plant Traits ◽  
White Pine ◽  
Multispectral Data ◽  
Reflected Light ◽  
Significant Difference ◽  
Research Goal ◽  
The Impact

&lt;p&gt;Spectral imaging of vegetation for phenotyping is a fast-developing field that enables fast, objective and automated assessment of plant traits. Advances in instrumentation allow collection of ever more detailed observations using hyperspectral imaging. This technique captures the reflected light in 100+ wavelengths, compared to multispectral sensors which typically obtain 3 to 5 wavelengths. With machine learning and careful statistical analysis these data can be efficiently transformed into predictions of crop health, yield, etc. However, these instruments are costly to acquire and produce volumes of data which are expensive to handle and archive, and therefore we must ask the question whether/when the investment is worth it. In this case study, we assess the implications of using hyperspectral vs multispectral imaging when monitoring the effects of an invasive fungal pathogen on seedlings of southwestern white pine. We discuss the impacts in terms of the complexity level of the research goals. Firstly, we discuss the impact on the accuracy and timing of infection detection. Pre-symptomatic detection of infection is possible using hyperspectral. To what extent is this possible using multispectral? Next, what is the trade-off between the two spectral methods when predicting for symptom severity? And lastly, the study contains a third level of complexity, a variety in genotypes. Using hyperspectral we can successfully separate the genotypes. However, is there still a significant difference in reflectance between genotypes when using multispectral data? This study shows that the need for hyperspectral depends on the complexity of the research goal, and therefore collecting more data might not always be useful.&lt;/p&gt;

Sensitivity and Specificity of Multispectral Imaging For Polypoidal Choroidal Vasculopathy.

Retina ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Feiyan Ma ◽  
Mingzhen Yuan ◽  
Igor Kozak ◽  
Qing Zhang ◽  
Youxin Chen
Keyword(s):  
Sensitivity And Specificity ◽  
Polypoidal Choroidal Vasculopathy ◽  
Multispectral Imaging ◽  
Choroidal Vasculopathy

scholarly journals Evaluating the Suitability of Hyper- and Multispectral Imaging to Detect Foliar Symptoms of the Grapevine Trunk Disease Esca in Vineyards

2020 ◽  
Author(s):  
Nele Bendel ◽  
Anna Kicherer ◽  
Andreas Backhaus ◽  
Hans-Christian Klück ◽  
Udo Seiffert ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Original Data ◽  
Economic Losses ◽  
Disease Detection ◽  
Field Detection ◽  
Multispectral Data ◽  
True Incidence ◽  
Grapevine Trunk Diseases

Abstract Background: Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging.Results: Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult.Conclusions: In this study, different approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.

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