Extracted features based multi-class classification of orthodontic images

The purpose of this study is to investigate computer vision and machine learning methods for classification of orthodontic images in order to provide orthodontists with a solution for multi-class classification of patients’ images to evaluate the evolution of their treatment. Of which, we proposed three algorithms based on extracted features, such as facial features and skin colour using YCbCrcolour space, assigned to nodes of a decision tree to classify orthodontic images: an algorithm for intra-oral images, an algorithm for mould images and an algorithm for extra-oral images. Then, we compared our method by implementing the Local Binary Pattern (LBP) algorithm to extract textural features from images. After that, we applied the principal component analysis (PCA) algorithm to optimize the redundant parameters in order to classify LBP features with six classifiers; Quadratic Support Vector Machine (SVM), Cubic SVM, Radial Basis Function SVM, Cosine K-Nearest Neighbours (KNN), Euclidian KNN, and Linear Discriminant Analysis (LDA). The presented algorithms have been evaluated on a dataset of images of 98 different patients, and experimental results demonstrate the good performances of our proposed method with a high accuracy compared with machine learning algorithms. Where LDA classifier achieves an accuracy of 84.5%

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.</p

PhysioNet 2012 Challenge: Predicting mortality of ICU patients using a cascaded SVM-GLM paradigm

The focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of inhospital mortality using general descriptors recorded at the time of admission to the ICU and up to 37 time-series measurements collected during the first 48 hours after admission. We developed an algorithm that uses both general descriptors and time-series measurements to predict the in-hospital death (IHD) of ICU patients in Event 1, and to provide a probability estimate of IHD in Event 2. Both aggregated variables and general descriptors were used as features of quadratic Support Vector Machine (SVM) classifiers. Six SVMs were trained using, for each one, all the positive examples plus, in turn, one sixth of the negative examples in the training set. Finally, a Generalized Linear Model with probit link was used to predict the probability of IHD for Event 2 using the raw outputs of the six SVMs as regressors. A positive binary prediction of IHD for Event 1 was made when the probability estimate was higher than an optimized threshold. Official final results of the challenge reported that our entry achieved an Event 2 score of 17.88, which is the best score out of the total 23 submissions, and Event 1 score of 0.5345 (second best score). © 2012 CCAL

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images.

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model

ASSESSMENT OF RISK IN PRETERM INFANTS USING POINT PROCESS AND MACHINE LEARNING APPROACHES

Preemies, infants who are born too soon, have a higher incidence of Life-Threatening Events (LTE’s) such as apnea (cessation of breathing), bradycardia (slowing of heart rate) and hypoxemia (oxygen desaturation) also termed as ABD (Apnea, Bradycardia, and Desaturation) events. Clinicians at Neonatal Intensive Care Units (NICU) are facing the demanding task of assessing the risk of infants based on their physiological signals. The aim of this thesis is to develop a risk stratification algorithm using a machine-learning framework with the features related to pathological fluctuations derived from point process model that will be embedded into the current physiological recording system to assess the risk of life-threatening events well in advance of occurrence in individual infants in the NICU. We initially propose a point process algorithm of heart rate dynamics for risk stratification of preterm infants. Based on this analysis, point process indices were tested to determine whether they were useful as precursors for life-threatening events. Finally, a machine-learning framework using point process indices as precursors were designed and tested to classify the risk of preterm infants. This work helps to predict the number of bradycardia events, N, in the subsequent hours measuring point process indices for the current hour. The model proposed uses Quadratic Support Vector Machine (QSVM), a machine learning classifier, which can solve class optimization problems and execute data at an exponential speed with higher accuracy for risk assessment that might facilitate effective management and treatment for preterm infants in NICU. The findings are relevant to risk assessment by analyzing the fluctuations in physiological signals that can act as precursors for the future life-threatening events

Toward finding the best machine learning classifier for LIBS-based tissue differentiation

Lasers have become generally accepted devices in surgical applications, especially as a cutting tool, for cutting both soft and hard tissues including bone (laserosteotomy). It has been shown that applying lasers in osteotomy have important advantages over mechanical tools, including faster healing, more precise cut and functional cutting geometries as well as less trauma [1, 2]. However, the ability of detecting the type of tissue that being cut during surgery can extend the application and safety of laserosteotomes in practice. As a result, the laser could be stopped automatically in case of cutting a tissue that should be preserved. Authors have previously demonstrated that laser-induced breakdown spectroscopy (LIBS) is a potential candidate to differentiate surrounding soft tissue from the bone in ex vivo condition [3]. In the current study, different machine learning classifiers were examined to find the best possible method to differentiate bone from soft tissues based on LIBS data. These methods include decision tree, K Nearest Neighbor (KNN), linear and quadratic Support Vector Machine (SVM) as well as linear and quadratic discriminant analysis. All classifiers were applied on LIBS data obtained from bone, muscle, and fat tissues using an Nd:YAG laser and an Echelle spectrometer. Confusion matrix and Receiver Operating Characteristic (ROC) curve were obtained for each classifier afterwards. Moreover, in order to estimate the model's performance on new data and also to protect the model against overfitting, cross-validation was applied. All mentioned examinations were performed with MATLAB (R2017b)

Human activity and posture classification using smartphone sensors and Matlab mobile

Human Activity Recognition (HAR) is significant, especially in the medical field. Activity recognition has been used in various ways as technology has advanced, particularly using a smartphone-based approach. This work aims to evaluate the accuracy of the triaxial accelerometer in the Matlab Mobile and examine the development and performance of the algorithms in identifying human motions on individuals of similar ages and physical appearances. Motion signals from three subjects are measured, data is preprocessed using a filtering technique, features are extracted, feature normalization is used to reduce bias in data measurement, and activities are classified. Confusion matrix, precision, recall, accuracy, F1-score, and Kappa score are performance indicators used to determine this classification approach. As a result, this research discovered that the Quadratic Support Vector Machine (SVM) produces the best results, with a 99.22 % accuracy rate, proving the efficacy of its activity identification method

A novel high-throughput and label-free phenotypic drug screening approach: MALDI-TOF mass spectrometry combined with machine learning strategies

A renewed and growing interest in phenotypic drug screening approaches in the field of drug discovery is observed, as it has become apparent that target-oriented drug discovery assays have inherent limitations and cannot fulfil the urgent unmet medical need for novel drugs. The shortcomings of target-oriented drug screening assays are especially apparent in the field of antibiotic drug discovery, where target-based approaches largely failed to translate screening hits to clinically relevant drugs. In this thesis, a proteomics-based phenotypic drug screening approach using MALDI-TOF mass spectrometry was developed, which is able to detect sub-lethal stress in bacterial cells provoked by antibiotics. To achieve this, mass spectra of whole-cells exposed to known antibiotics at concentrations below the minimal inhibitory concentration (MIC) were used to extract relevant mass spectral peaks with a data-dependent and automated computational pipeline created in the MATLAB environment. Using the selected subset of mass spectral peaks, classification models were trained to recognize general mass spectral responses provoked by unknown drugs in the cellular proteome. Additionally, the classification models proved capable of identifying the mechanisms of action of unknown drugs. To establish and validate the best performing classification modeling procedure, four different feature selection algorithms and nine classification models were analyzed in detail using an Escherichia coli data set composed of over 900 spectra, involving 17 antibiotics with four different mechanisms of action, at concentrations ranging 1×MIC down to 1/32×MIC in a two-fold dilution series. Four different feature selection approaches were investigated to ensure the extraction of relevant mass spectral data in response to the different antibiotics for classification modeling. The selection approaches included (1) a random forest of decision trees, (2) sequential forward feature selection, and (3) sequential backward feature selection. Mass spectral peaks selected by two or all three of these feature selection approaches were combined into (4) an aggregated feature set. Classification models were trained for all combinations of nine model types and the four feature sets. In this thesis two classification problems were investigated. First, a binary classification problem, to differentiate between affected cells, and non-affected cells based on selected mass spectral peaks. Second, a multi-class model was trained to detect and distinguish between the different antibiotic mechanisms of action, a highly desired drug screening assay characteristic. The combination of these elements yielded 72 models, which were evaluated based on their overall classification accuracy. The overall classification accuracy was determined using internal 10-fold cross-validation and external validation, which was performed with a blind set of 20 drugs. The internal and external validation studies showed that the aggregated feature set in combination with a quadratic support vector machine-based model (Q-SVM) resulted in the best classification performance. For the E. coli data set, this was represented by an overall accuracy of 0.92 for internal validation and an accuracy of 0.95 for the external validation of the Q-SVM model. Classifying based on the mechanism of action of the antibiotics resulted in a classification accuracy of 0.67 for internal validation and 0.80 for external validation. Furthermore, it was shown that the peak selection method was able to identify relevant, known stress associated proteins within the aggregated feature sets of both the binary and the mechanism of action model. After the experimental workflow and the computational pipeline were established based on E. coli data, the method was applied to four different organisms (the Gram-positive bacterium Staphylococcus aureus, the fungi Saccharomyces cerevisiae and Candida albicans, and human HeLa cancer cell line) and different proteomic responses, to explore the versatility and transferability of the developed screening assay. The applicability of the method was demonstrated by the consistent performance of the classification models generated with the experimental and computational pipeline. This resulted in binary model accuracies between 0.92 and 0.97 for internal and 0.77 and 0.95 for external validation, depending on the assayed organism and data set complexity. For mechanism of action models, model accuracies ranged between 0.73 and 0.96 for internal and 0.66 and 0.93 for external validation. The application of the developed assay on different organisms with different drug stressors highlighted several advantageous characteristics of the developed MALDI-TOF MS screening approach. Both the binary and mechanism of action classification models of S. aureus correctly identified an antibiotic drug (fusidic acid) in the blind test set, which had a target binding activity that was not present in the training data set. This implicates the ability of the method to detect novel drugs within known global mechanism of action for which the model was trained. Moreover, external validation of S. cerevisiae showed that the binary classification model is able to detect antifungal drugs (tavaborole, an antifungal protein synthesis inhibitor) with a mechanism of action which was not present in the training data set. This is a highly desirable property of any phenotypic screening assay, as it shows that the assay allows for the identification of drugs with novel mechanisms of action. Lastly, the proteomic effect of different types of drugs on mammalian cells was explored by using the HeLa cancer cell line. It was shown that the presented proteomic profiling approach can easily detect several types of drug-induced stresses in HeLa cells, in particular corticosteroids and tubulin (de)polymerization inhibitors, but is less suitable for distinguishing other types of drug classes (neurotransmitter antagonists, statins, opioids). Additionally, the application of the assay on HeLa cells demonstrated the ability to detect different types of stresses, such as the cells’ proteomic response to UV exposure or heat shocks. These results pave the way for possible distinction between apoptosis and necrosis pathways in HeLa cells using the presented MALDI-TOF MS based method. To conclude, a high-throughput compatible, label free, MALDI-TOF mass spectrometry-based screening assay is described in this thesis, which measures sub-lethal drug effects on the cellular proteome in a phenotypic and pharmacological relevant setting. The method was found suitable for whole-cell screening of small libraries of drugs, and showed the ability to distinguish different types of stresses elicited on multiple types of cell cultures. The potential to find new, weakly active drugs within a known mechanism of action, as well as the ability to detect sub-lethal drug responses with new mechanisms of action for which the model was not trained, was demonstrated. The characteristic to identify novel mechanisms of action in a cell-based screen can be exploited to solve the most pressing issues in drug discovery today. In addition, mechanistic information of the drugs activity can be used as a starting point for further target elucidation or to prioritize drug screening hits. The studies performed in this thesis have resulted in a solid foundation for further research that expand the capabilities of the MALDI-TOF MS-based assay in a broad range of phenotypic profiling applications in the drug discovery field

Harvesting Intelligence: A Comprehensive Study on Transforming Aquaponic Agriculture with AI and IoT

Aquaponics, an agricultural technique that merges aquaculture and hydroponics, is on the brink of a transformative advancement with the amalgamation of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT). The incorporation of these cutting edge technologies in the field of aquaponics is bringing about a profound transformation in the realm of sustainable agriculture. This extensive investigation delves into the profound influence of these cutting-edge technologies on aquaponics, with a focus on predictive analysis, system optimization, environmental monitoring, and disease prevention. By means of ML and DL algorithms, historical and real-time data are scrutinized in order to forecast environmental fluctuations, optimize resource allocation, and facilitate the growth of crops and fish. IoT devices consistently gather data pertaining to crucial parameters, thereby enabling real-time monitoring and control of the aquaponic system. Furthermore, IoT technology enhances resource utilization and grants the ability to remotely monitor and manage the system. The detection of abnormalities in fish behavior and plant health through the utilization of ML and DL algorithms allows for the implementation of proactive measures aimed at preventing outbreaks and minimizing losses. Furthermore, these advanced technologies also offer personalized recommendations for effective management of various crop and fish species. The incorporation of ML, DL, and IoT into the field of aquaponics signifies a substantial advancement towards a more sustainable, efficient, and productive form of agriculture. These innovative technologies possess the capability to effectively address the challenges associated with global food security by optimizing the utilization of resources, maintaining environmental equilibrium, and mitigating the occurrence of disease outbreaks. In the context of the examined research endeavors presented in this article, it is anticipated that the utilization of smart control units in conjunction with the aquaponics system will yield greater profitability, increased intelligence, enhanced precision, and heightened efficacy. In the context of the examined research endeavors presented in this article, it is anticipated that the utilization of ML, DL and IoT in conjunction with the aquaponics system will yield greater profitability, increased intelligence, enhanced precision, and heightened efficacy