Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

Imaging-based high-content screens often rely on single cell-based evaluation of phenotypes in large data sets of microscopic images. Traditionally, these screens are analyzed by extracting a few image-related parameters and use their ratios (linear single or multiparametric separation) to classify the cells into various phenotypic classes. In this study, the authors show how machine learning–based classification of individual cells outperforms those classical ratio-based techniques. Using fluorescent intensity and morphological and texture features, they evaluated how the performance of data analysis increases with increasing feature numbers. Their findings are based on a case study involving an siRNA screen monitoring nucleoplasmic and nucleolar accumulation of a fluorescently tagged reporter protein. For the analysis, they developed a complete analysis workflow incorporating image segmentation, feature extraction, cell classification, hit detection, and visualization of the results. For the classification task, the authors have established a new graphical framework, the Advanced Cell Classifier, which provides a very accurate high-content screen analysis with minimal user interaction, offering access to a variety of advanced machine learning methods.

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Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study (Preprint)

10.2196/preprints.20641 ◽

2020 ◽

Author(s):

Eunjeong Park ◽

Kijeong Lee ◽

Taehwa Han ◽

Hyo Suk Nam

Keyword(s):

Machine Learning ◽

Medical Staff ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Grading System ◽

Operating Characteristics ◽

Stroke Patients ◽

Motor Weakness ◽

Automatic Grading

BACKGROUND Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers. OBJECTIVE In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff. METHODS We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). RESULTS The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification. CONCLUSIONS The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

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Extraction of Multiple Diseases in Apple Leaf Using Machine Learning

International Journal of Image and Graphics ◽

10.1142/s021946782140009x ◽

2021 ◽

pp. 2140009

Author(s):

Swati Singh ◽

Sheifali Gupta ◽

Ankush Tanta ◽

Rupesh Gupta

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Apple Scab ◽

Region Of Interest ◽

Texture Features ◽

Histogram Equalization ◽

K Nearest Neighbor ◽

Plant Leaf ◽

Diseased Part ◽

Fuzzy Histogram

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using K nearest neighbor classifier.

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Analysis of internet of things malware using image texture features and machine learning techniques

Internet of Things ◽

10.1016/j.iot.2019.100153 ◽

2020 ◽

Vol 9 ◽

pp. 100153 ◽

Cited By ~ 2

Author(s):

Evanson Mwangi Karanja ◽

Shedden Masupe ◽

Mandu Gasennelwe Jeffrey

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Texture Features ◽

Machine Learning Techniques ◽

Image Texture ◽

Learning Techniques

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Glioma grading using a machine‐learning framework based on optimized features obtained from T 1 perfusion MRI and volumes of tumor components

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.26704 ◽

2019 ◽

Vol 50 (4) ◽

pp. 1295-1306 ◽

Cited By ~ 6

Author(s):

Anirban Sengupta ◽

Anandh K. Ramaniharan ◽

Rakesh K. Gupta ◽

Sumeet Agarwal ◽

Anup Singh

Keyword(s):

Machine Learning ◽

Perfusion Mri ◽

Learning Framework ◽

Glioma Grading

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Comparison of Machine Learning Algorithms for Skin Disease Classification Using Color and Texture Features

2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) ◽

10.1109/icacci.2018.8554512 ◽

2018 ◽

Cited By ~ 2

Author(s):

Parameshwar R. Hegde ◽

Manjunath M. Shenoy ◽

B.H. Shekar

Keyword(s):

Machine Learning ◽

Skin Disease ◽

Learning Algorithms ◽

Texture Features ◽

Machine Learning Algorithms ◽

Disease Classification ◽

Color And Texture Features

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The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia

Scientific Reports ◽

10.1038/s41598-020-76141-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Hui-Bin Tan ◽

Fei Xiong ◽

Yuan-Liang Jiang ◽

Wen-Cai Huang ◽

Ye Wang ◽

...

Keyword(s):

Machine Learning ◽

Predictive Value ◽

Laboratory Data ◽

Texture Features ◽

Chest Ct ◽

Automatic Machine ◽

Classification Model ◽

Control Group ◽

Ct Image ◽

Severe Group

Abstract To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.

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