ELM ZA KLASIFIKACIJU TUMORA MOZGA KOD 3D MR SNIMAKA

Extreme Learning machine (ELM) a widely adopted algorithm in machine learning field is proposed for the use of pattern classification model using 3D MRI images for identifying tissue abnormalities in brain histology. The four class classification includes gray matter, white matter, cerebrospinal-fluid and tumor. The 3D MRI assessed by a pathologist indicates the ROI and the images are normalized. Texture features for each of the sub-regions is based on the Run-length Matrix, Co-occurence Matrix, Intensity, Euclidean distance, Gradient vector and neighbourhood statistics. Genetic Algorithm is custom designed to extract and sub-select a decisive optimal bank of features which are then used to model the ELM classifier and best selection of ELM algorithm parameters to handle sparse image data. The algorithm is explored using different activation function and the effect of number of neurons in the hidden layer by using different ratios of the number of features in the training and test data. The ELM classification outperformed in terms of accuracy, sensitivity and specificity as 93.20 %, 91.6 %, and 97.98% for discrimination of brain and pathological tumor tissue classification against state-of-the-art feature extraction methods and classifiers in the literature for publicly available SPL dataset.ELM, široko prihvaćen algoritam strojnog učenja se predlaže za korištenje u uzorkovanju pomoću klasifikacijskog modela 3D MRI slika za identifikaciju abnormalnosti tkiva u histologiji mozga. Četiri klase obuhvaćaju sive, bijele tvari, cerebrospinalne tekućine-i tumore. 3D MRI koji ocjenjuje patolog, ukazuje na ROI, a slike su normalizirane. Značajke tekstura za svaku od podregija se temelje na Run-length matrici, ponovnom pojavljivanju matrice, intenzitet, euklidska udaljenost, gradijent vektora i statistike susjedstva. Genetski algoritam je obično dizajniran za izdvajanje i sub-optimalan odabir odlučujući o značajkama koje se onda koriste za model ELM klasifikatora i najbolji izbor ELM parametra algoritama za obradu rijetkih slikovnih podataka. Algoritam se istražuje koristeći različite aktivacijske funkcije i utjecaj broja neurona u skrivenom sloju pomoću različitih omjera broja značajki kod trening i test podataka. ELM klasifikacija je nadmašila u smislu točnosti, osjetljivosti i specifičnosti, kao 93,20%, 91,6% i 97,98% za diskriminaciju mozga i patološki kod tumora i sistematizacije metode za prikupljanje podataka i klasifikatore u literaturi za javno dostupne SPL skup podataka

A Hybrid DE-RGSO-ELM for Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images

Medical diagnostics, a technique used for visualizing the internal structures and functions of human body, serves as a scientific tool to assist physicians and involves direct use of digital imaging system analysis. In this scenario, identification of brain tumors is complex in the diagnostic process. Magnetic resonance imaging (MRI) technique is noted to best assist tissue contrast for anatomical details and also carries out mechanisms for investigating the brain by functional imaging in tumor predictions. Considering 3D MRI model, analyzing the anatomy features and tissue characteristics of brain tumor is complex in nature. Henceforth, in this work, feature extraction is carried out by computing 3D gray-level cooccurence matrix (3D GLCM) and run-length matrix (RLM) and feature subselection for dimensionality reduction is performed with basic differential evolution (DE) algorithm. Classification is performed using proposed extreme learning machine (ELM), with refined group search optimizer (RGSO) technique, to select the best parameters for better simplification and training of the classifier for brain tissue and tumor characterization as white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and tumor. Extreme learning machine outperforms the standard binary linear SVM and BPN for medical image classifier and proves better in classifying healthy and tumor tissues. The comparison between the algorithms proves that the mean and standard deviation produced by volumetric feature extraction analysis are higher than the other approaches. The proposed work is designed for pathological brain tumor classification and for 3D MRI tumor image segmentation. The proposed approaches are applied for real time datasets and benchmark datasets taken from dataset repositories

DeepSegNet: An Innovative Framework for Accurate Blood Cell Image Segmentation

Image segmentation plays a crucial and indispensable role in computer vision, as it allows the partitioning of an image into meaningful regions or objects. Among its numerous applications, image segmentation holds particular significance in the domains of medical diagnosis and healthcare. Its vital role in this field stems from its ability to extract and delineate specific anatomical structures, tumors, lesions, and other critical regions from medical images. In medical diagnosis, accurate and precise segmentation of organs and abnormalities is paramount for effective treatment planning, disease monitoring, and surgical interventions. Blood cell image segmentation is highly valuable for medical diagnosis and research, particularly in the domains of hematology and pathology. Precisely segmenting blood cells from microscopic images is essential, as it offers critical insights into various blood-related disorders and diseases. Although deep learning segmentation models have exhibited promising results in blood cell image segmentation, they suffer from several limitations. These drawbacks encompass scarce data availability, inefficient feature extraction, extended computation time, limited generalization to unseen data, challenges with variations, and artifacts. Consequently, these limitations can adversely impact the overall performance of the models. Blood cell image segmentation encounters persistent challenges due to factors like irregular cell shapes, which pose difficulties in boundary delineation, imperfect cell separation in smears, and low cell contrast, leading to visibility issues during segmentation. This research article introduces the innovative DeepSegNet framework, a powerful solution for precise blood cell image segmentation. The performance of widely-used segmentation models like PSPNet, FPN, and DeepLabv3+ is enhanced through the use of sophisticated preprocessing techniques, improving generalization capability, data diversity, and training stability. Additionally, the incorporation of diverse dilated convolutions and feature fusion further contributes to the improvement of these models. The Improved PSPNet, Improved FPN, Deep Lab V3, and Improved Deep Lab V3+ achieved 98.25%, 99.04%, 98.23%, and 99.31% accuracy, respectively, and the Improved Deep Lab V3+ model outperformed well and produced a Dice Coefficient of 99.32% and Precision of 99.38%. The proposed DeepSegNet framework improves overall performance with an increased accuracy of 8.91%, 3.72%, 17.73%, 22.83%, 7.96%, 9.61%, 17.36%, 6.22%, 13.32%, and 14.32% compared to the existing models. This framework, which can be applied to accurately identify and quantify different cell types from blood cell images, is instrumental in diagnosing a variety of hematological disorders and diseases

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

ELM ZA KLASIFIKACIJU TUMORA MOZGA KOD 3D MR SNIMAKA

Extreme Learning machine (ELM) a widely adopted algorithm in machine learning field is proposed for the use of pattern classification model using 3D MRI images for identifying tissue abnormalities in brain histology. The four class classification includes gray matter, white matter, cerebrospinal-fluid and tumor. The 3D MRI assessed by a pathologist indicates the ROI and the images are normalized. Texture features for each of the sub-regions is based on the Run-length Matrix, Co-occurence Matrix, Intensity, Euclidean distance, Gradient vector and neighbourhood statistics. Genetic Algorithm is custom designed to extract and sub-select a decisive optimal bank of features which are then used to model the ELM classifier and best selection of ELM algorithm parameters to handle sparse image data. The algorithm is explored using different activation function and the effect of number of neurons in the hidden layer by using different ratios of the number of features in the training and test data. The ELM classification outperformed in terms of accuracy, sensitivity and specificity as 93.20 %, 91.6 %, and 97.98% for discrimination of brain and pathological tumor tissue classification against state-of-the-art feature extraction methods and classifiers in the literature for publicly available SPL dataset.ELM, široko prihvaćen algoritam strojnog učenja se predlaže za korištenje u uzorkovanju pomoću klasifikacijskog modela 3D MRI slika za identifikaciju abnormalnosti tkiva u histologiji mozga. Četiri klase obuhvaćaju sive, bijele tvari, cerebrospinalne tekućine-i tumore. 3D MRI koji ocjenjuje patolog, ukazuje na ROI, a slike su normalizirane. Značajke tekstura za svaku od podregija se temelje na Run-length matrici, ponovnom pojavljivanju matrice, intenzitet, euklidska udaljenost, gradijent vektora i statistike susjedstva. Genetski algoritam je obično dizajniran za izdvajanje i sub-optimalan odabir odlučujući o značajkama koje se onda koriste za model ELM klasifikatora i najbolji izbor ELM parametra algoritama za obradu rijetkih slikovnih podataka. Algoritam se istražuje koristeći različite aktivacijske funkcije i utjecaj broja neurona u skrivenom sloju pomoću različitih omjera broja značajki kod trening i test podataka. ELM klasifikacija je nadmašila u smislu točnosti, osjetljivosti i specifičnosti, kao 93,20%, 91,6% i 97,98% za diskriminaciju mozga i patološki kod tumora i sistematizacije metode za prikupljanje podataka i klasifikatore u literaturi za javno dostupne SPL skup podataka

Machine Learning Based Power Estimation for CMOS VLSI Circuits

Nowadays, machine learning (ML) algorithms are receiving massive attention in most of the engineering application since it has capability in complex systems modeling using historical data. Estimation of power for CMOS VLSI circuit using various circuit attributes is proposed using passive machine learning-based technique. The proposed method uses supervised learning method, which provides a fast and accurate estimation of power without affecting the accuracy of the system. Power estimation using random forest algorithm is relatively new. Accurate estimation of power of CMOS VLSI circuits is estimated by using random forest model which is optimized and tuned by using multiobjective NSGA-II algorithm. It is inferred from the experimental results testing error varies from 1.4% to 6.8% and in terms of and Mean Square Error is 1.46e-06 in random forest method when compared to BPNN. Statistical estimation like coefficient of determination (R) and Root Mean Square Error (RMSE) are done and it is proven that random Forest is best choice for power estimation of CMOS VLSI circuits with high coefficient of determination of 0.99938, and low RMSE of 0.000116

Design and Implementation of a Machine Learning-Based Wind Turbine Control System

The Machine Learning-Based Wind Turbine Control System (MLBWTCS) is a new technology that uses machine learning algorithms to optimize the performance of wind turbines. The system collects data from sensors installed on the wind turbine to monitor various variables such as wind speed, blade pitch angle, generator torque, and power output. The data collected is preprocessed and fed into a machine learning model, which predicts the optimal settings for the turbine operations. The predictions are then used to control the operations of the wind turbine in real-time. The MLBWTCS has been shown to improve the efficiency and reliability of wind turbines, resulting in increased power generation and reduced maintenance costs. This paper presents a detailed description of the design and implementation of the MLBWTCS, including data collection, preprocessing, feature selection and machine learning model selection

Ochronosis

Ochronosis is a rare disorder, which presents with distinct clinical and biochemical features. A fifty seven year old male presented with fracture femur, osteoarthritis, Oslerâ€s sign, alkaptonuria and cutaneous ochronosis. Though the clinical progression of his alkaptonuria was typical, he presented interesting features including non-uniting fracture and arthritis of big and small joints