Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

BackgroundAccurate segmentation of pelvic bones is an initial step to achieve accurate detection and localisation of pelvic bone metastases. This study presents a deep learning-based approach for automated segmentation of normal pelvic bony structures in multiparametric magnetic resonance imaging (mpMRI) using a 3D convolutional neural network (CNN).MethodsThis retrospective study included 264 pelvic mpMRI data obtained between 2018 and 2019. The manual annotations of pelvic bony structures (which included lumbar vertebra, sacrococcyx, ilium, acetabulum, femoral head, femoral neck, ischium, and pubis) on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images were used to create reference standards. A 3D U-Net CNN was employed for automatic pelvic bone segmentation. Additionally, 60 mpMRI data from 2020 were included and used to evaluate the model externally.ResultsThe CNN achieved a high Dice similarity coefficient (DSC) average in both testing (0.80 [DWI images] and 0.85 [ADC images]) and external (0.79 [DWI images] and 0.84 [ADC images]) validation sets. Pelvic bone volumes measured with manual and CNN-predicted segmentations were highly correlated (R2 value of 0.84–0.97) and in close agreement (mean bias of 2.6–4.5 cm3). A SCORE system was designed to qualitatively evaluate the model for which both testing and external validation sets achieved high scores in terms of both qualitative evaluation and concordance between two readers (ICC = 0.904; 95% confidence interval: 0.871–0.929).ConclusionsA deep learning-based method can achieve automated pelvic bone segmentation on DWI and ADC images with suitable quantitative and qualitative performance.

Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps

Machine learning (ML) is also seen as an advanced technique that is only usable by highly qualified specialists. This prohibits this instrument from being utilized by many doctors and biologists in their studies. This paper’s purpose is to eradicate this obsolete perception. We claim that the recent creation of advanced high-performance ML techniques helps biomedical researchers to create competitive ML models rapidly without needing in-depth knowledge of the algorithms underlying them. This advanced system is implemented used best programming tool Python including two parts. Firstly, feature engineering and preprocessing with the Neighborhood Cleaning Rule (NCL) high-performance re-sampling procedure. Second, advanced models for high-performance machine learning, including AutoML, advanced XGBoost, and advanced ensemble bagging models. Finally, we believe that our developments would improve the way doctors interpret machine learning utilizing sophisticated and high-performance machine learning technologies and facilitate broad clinical use of Artificial Intelligence (AI) techniques.

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

Despite the promise for performance and accuracy improvements of quantum inspired (QI) algorithms over classical machine learning (ML) algorithms, such gains have not been realized in practice. The quantum inspired algorithms can theoretically achieve significant speed up based on sampling assumptions and have thus far failed to outperform the existing classical ML models in practical applications. The speedup of quantum machine learning (QML) algorithms assume the access to data in quantum random access memory (QRAM) which is a strong assumption with current quantum architectures. QI algorithms assume sample and query (SQ) access to input vector and norms of matrices using a dynamic data structure. We explore the components of these models and the assumptions in this paper by surveying the recent works in QML and QI Machine learning (QIML) algorithms. We limit our study to QML and QIML models on achieving a speed up over classical ML techniques rather than individual proofs of these algorithms. This study highlights the assumptions being made that are currently not practical for QML and QIML algorithms in achieving performance advantage over classical ML algorithms.

Systematic literature review of machine learning based software development effort estimation models

BackgroundEarly 30-day readmission remains a significant burden on the socioeconomic and healthcare system in the context of decompensated cirrhosis. Early recognition and accurate identification are crucial. However, current evidence is elusive and traditional scores concerning liver disease severity are lacking specificity and sensitivity. We sought to construct and validate an explainable machine learning (ML)-based prediction model, and evaluate its prognostic implementation in patients readmitted due to acute episodes. The prediction model for discovery and validation was based on a two-center prospective investigation. Our discovery sample, comprising 636 patients with cirrhosis, was divided into a training set and a test set, with an additional cohort of 150 patients serving as an external validation. Eleven ML methods were performed to establish an indicative model based on a variety of easily accessible and obtainable variables from the electronic health record. The area under the ROC curve (AUC), alongside several evaluation parameters, was used for comparison regarding predictive performance. Considering feature importance and final model explanation, we adopted the SHapley Additive exPlanation method for ranking. Furthermore, prognostic implementation was verified by subgrouping according to the final model and clinical outcomes during follow-up.ResultsAmong all 11 ML algorithms, the random forest (RF) algorithm represented the best discriminatory capability. Processing feature reduction generated a final 7-feature RF model with explainability based on the importance ranking. Our constructed model was of moderately accurate prediction pertaining to internal and external validations, with respective AUCs of 0.853 and 0.838, which was further transformed into an online tool to facilitate daily practice. Patients positively adjudged by the prediction model had aggravated underlying disease severity and poor psychophysiologic reservation.ConclusionsThe final explainable ML model was capable of predicting early readmission and was closely connected with adverse outcomes in individual patients experiencing decompensated cirrhosis. Notably, it allayed the “black-box” concerns inherent to ML techniques with an indirect interpretation.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13040-025-00479-0.

Objective To investigate the clinical and imaging features of mild encephalitis/encephalopathy with a reversible splenial lesion (MERS). Methods The clinical and imaging data of 11 patients diagnosed with MERS were retrospectively analyzed, including 4 adults and 7 adolescents and children. The clinical and imaging features of these patients were summarized. Results Eleven MERS patients had a history of precursor infection. Encephalopathy symptoms included headache and drowsy in 6 cases, disturbance of consciousness in 3, epilepsy in 2, illusion in 2 and delirium in 1. The symptoms of all the MERS patients obviously alleviated in 2 weeks. MRI showed all cases as isolation of splenium of corpus callosum lesions, including 9 cases of circular abnormal signals in central of splenium of corpus callosum and 2 cases of arc-shaped abnormal signals in splenium of corpus callosum. The abnormal signal showed high-signal on T2-weighted and diffusion-weighted with low-signal on apparent diffusion coefficient images in the splenium of corpus callosum. After the onset of 4 weeks, MRI returned to normal, and the corpus callosum of abnormal signal lesions disappeared. Conclusions Most patients have a history of precursor infection before the onset of the MERS. The clinical manifestations of MERS patients include encephalopathy symptoms such as headache, disturbance of consciousness, seizures, illusion, etc. MRI shows circular or arc-shaped lesions in splenium of corpus callosum, which demonstrate high-signal on diffusion-weighted and low-signal on apparent diffusion coefficient images. Prognosis of MERS patients is mostly good. Some patients' lesions on MRI appear later than the onset of encephalopathy symptoms. Key words: Encephalitis; Consciousness disorders; Epilepsy; Corpus callosum; Magnetic resonance imaging

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

Sensitivity evaluation of machine learning-based calibrated transportation mode choice models: A case study of Alexandria City, Egypt

Machine learning (ML) techniques play a significant role in detecting abnormal link operations in wireless networks. However, during the design and development process of a ML-based model, numerous factors including input data transformation, scaling methods, choice of ML techniques and their associated parametrization, influence the final model performance. In this paper, we discuss the design and development process of wireless link anomaly detection models followed by the model ranking process for selecting the most suitable models given the required application criteria. Under this premise, we consider four different data representations, four anomaly types, six scaling methods and six distinct ML techniques along with their associated parametrization trials leading to over twenty thousand anomaly detection models for wireless links. We show that the most suitable supervised ML models are able to detect anomalies with an F1 score of at least 88% and up to perfect score, whereas unsupervised ML models can attain an F1 score of up to 96% depending on the anomaly type, and on average, amongst 20,352 available models, the supervised SVM model can be readily adopted to efficiently detect any type of anomaly with an F1 score of at least 94%, particularly when trained on encoded data representations using the combination of "mean and deviation scaling" and "non-linear RBF kernel".

Techno-economic estimation of a non-cover box solar still with thermoelectric and antiseptic nanofluid using machine learning models

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

Mechanical design is one of the essential disciplines in engineering applications, while inspirations of design ideas highly depend on the ability and prior knowledge of engineers or designers. With the rapid development of machine learning (ML) techniques, artificial intelligence (AI)‐based design methods are promising tools for the design of advanced engineering systems. So far, there have been some studies of 2D patterns and structural designs based on ML techniques. However, a particular challenge remains in allowing complex 3D mechanical designs using ML techniques. Herein, a novel and experience‐free method to equip ML models with 3D design capabilities by combining a convolutional neuron network (CNN) with a deep convolutional generative adversarial network (DCGAN) is developed. The model directly receives 2D image‐based training data that define the complex 3D structures of a specific machine part. After the training process, an infinite number of new 3D designs can be generated by the proposed model, with their geometric and mechanical properties being accurately predicted at the same time. Moreover, the generated new designs can be fed back to expand the original input datasets for further ML model training and updating.

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained.