Current Artificial Intelligence - Online First
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Local Mean Gradient Pattern (LMGP): A Novel Approach for the Classification of Brain CT Scan Images
Authors: Kavya Singh, Anil Kumar Koundal and Navjeet KaurAvailable online: 26 February 2025More LessObjectiveVisual descriptor methods like Local Binary Pattern (LBP) capture anatomical structures in captured images along with their disparities, which can be exploited by suitable methods for the diagnosis of medical anomalies. In our study, we have proposed a Local Mean Gradient Pattern (LMGP), based partly on LBP, a feature extraction algorithm for the classification of Computed Tomography (CT) images of the brain into normal, ischemic, or hemorrhage categories.
MethodsThe AISD and Kaggle datasets containing patients’ brain CT scan images [acute ischemic stroke, hemorrhage, and normal cases] were taken. Initially, adaptive histogram equalization (AHE) techniques were applied as preprocessing operations to enhance the quality of the CT images. Furthermore, features were extracted from the preprocessed data using several feature extraction techniques, including our proposed LMGP feature descriptor. The features were then scaled using the standard scaling technique. Subsequently, preprocessed images were fed into different classifiers to build models for classifying brain CT scan images.
ResultsThe effectiveness of our methodology LMGP was determined using different metrics, such as recall, precision, F1 score, logarithmic loss, accuracy (ACC), and area under the curve (ROC). Conclusively, LMGP performed best when the RBF-SVM classifier was used for the classification and gave an accuracy of 94% and 96% in the case of five-fold and ten-fold cross-validation, respectively.
ConclusionLMGP offers a distinctive and robust method of feature extraction from CT scan images by combining local information along with gradient change in pixels of the image. The efficacy of our proposed methodology (LMGP) was evaluated by using distinct classifiers, and the results were compared with eight different feature extraction methods. Overall, LMGP effectively outperformed all other feature descriptor methods in this study.
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Revolutionizing Patient Safety: Machine Learning and AI for the Early Detection of Adverse Drug Reactions and Drug-Induced Toxicity
Authors: Pratikeswar Panda and Rajaram MohapatraAvailable online: 21 October 2024More LessAdverse drug reactions and drug-induced toxicity provide significant issues in drug research, jeopardizing patient safety and driving up healthcare costs. Toxicity has a greater potential impact than infectious diseases since it is less visible. Early diagnosis of these difficulties is critical to determining a drug's safety and viability profile. The combination of machine learning and artificial intelligence has marked a watershed moment in the identification of early adverse drug reactions and toxicity. These computational approaches enable rapid, extensive, and precise prediction of likely adverse drug reactions and toxicity even before practical drug manufacture, preclinical testing, and clinical trials. This paradigm change strives to create more efficient and safe drugs, lowering the likelihood of drug withdrawal. This comprehensive review investigates the critical role of machine learning and artificial intelligence in quickly detecting adverse drug reactions and toxicity, including approaches from data mining to deep learning. It lists essential databases, modelling techniques, and software that may be used to model and predict a wide range of toxicities and adverse drug reactions. This review provides a comprehensive overview, outlining recent developments and projecting future opportunities in machine learning and artificial intelligence-driven rapid identification of adverse drug reactions and drug-induced toxicity. It highlights the capabilities of these technologies and their enormous potential to improve patient safety and revolutionize medication discovery.
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