Brain diseases, including traumas, tumors, haemorrhages, and other neurological abnormalities, present significant risks to human health and may result in life-threatening effects. Traditional healthcare often encount...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Brain diseases, including traumas, tumors, haemorrhages, and other neurological abnormalities, present significant risks to human health and may result in life-threatening effects. Traditional healthcare often encounters challenges in the concurrent identification of various disorders, hence limiting the therapeutic efficacy of identifying brain diseases using MRI analysis. This paper presents a multiple-label classification model tailored for brain disease diagnosis using a large-scale MRI dataset. Our approach employs multi-label classification, a methodology previously unexplored in this context, to uniquely address the challenge of identifying multiple diseases from a single MRI scan. Comprehensive experiments rigorously evaluated the proposed model, achieving state-of-the-art performance with an overall accuracy of 98.43%, surpassing existing models in terms of both diagnostic precision and computational efficiency. The results demonstrate the potential for more accurate, rapid, and efficient brain disease diagnosis, ultimately improving patient management. In addition to advancing the field of medical image analysis, our work paves the way for future research that aims to further enhance automated systems in healthcare diagnostics.
Lung cancer is a severe and often fatal disease characterized by uncontrolled cell growth in lung tissues. The high death rate associated with lung cancer presents a significant public health problem. In this study, o...
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ISBN:
(数字)9798331519094
ISBN:
(纸本)9798331519100
Lung cancer is a severe and often fatal disease characterized by uncontrolled cell growth in lung tissues. The high death rate associated with lung cancer presents a significant public health problem. In this study, our objectives are to develop novel models for lung cancer prediction using a diverse set of datasets, improving the outcomes of earlier studies on the problem. we addressed the critical issue of imbalanced datasets in lung cancer research by employing techniques such as undersampling and Synthetic Minority Over-sampling Technique (SMOTE) to achieve balanced datasets. We focused on feature selection techniques such as correlation based analysis, feature importance and coefficient based analysis to make efficient models. Different machine learning techniques have been employed, including decision trees, random forests, K-nearest neighbor, Gradient Boosting Machine, AdaBoost, XGBoost, LightGBM, CatBoost, logistic regression classifiers, deep neural networks, and a curriculum learning-based voting classifier. Among all the models evaluated, Random Forest and LightGBM demonstrated the highest accuracy, achieving a performance rate of 97%. Subsequently, we proposed a model using a voting classifier which uses multiple base learners such as Random Forest, GBM, XGBoost, Light GBM, CatBoost, and Deep Neural Network. This ensemble approach also achieved an accuracy rate of 97%. In conclusion, high-performance models for lung cancer prediction were developed, which could potentially help in the improvement of non-invasive lung cancer prediction models in medical settings.
The rapid evolution of machine learning (ML) has brought about groundbreaking developments in numerous industries, not the least of which is in the area of undersea communication. This domain is critical for applicati...
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The aim is to create a method for accurately estimating the duration of post-cancer treatment, particularly focused on chemotherapy, to optimize patient care and recovery. This initiative seeks to improve the effectiv...
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