Real-Time Imbalance Liver Tumor Sensor Databases: A Deep Classification Framework with Ensemble Feature Extraction, Ranking, and Probabilistic Segmentation for Efficient Analysis
Keywords:
Imbalance liver image data, probabilistic segmentation, deep learning, support vector machine, decision tree, ensemble learning modelAbstract
As the size of liver tumor image databases increases, it becomes challenging to enhance the true positive rate of traditional prediction approaches due to the high majority-minority ratio and noise. However, while 3D convolutions have the potential to fully leverage spatial information, they also come with high computational costs and require significant GPU memory usage. On the other hand, 2D convolutions are limited in their ability to utilize the information contained in the third dimension. Missing feature values, feature noise, and Imbalanced liver classes are some of the significant factors that can impact the quality of input data. The quality of imbalance data significantly impacts the efficiency of classification approaches, making it necessary to ensure high-quality input data to achieve optimal results. Therefore, to ensure high-quality predictions on imbalanced liver datasets, models need to be optimized. Sensors are commonly used to collect and measure physical parameters, and they can be used to obtain liver tumor data for the proposed model. In this work, medical imaging sensors such as CT (computed tomography) machines are used to capture detailed images of the liver and identify potential tumors. Therefore, sensors play a crucial role in the proposed model by providing the necessary data to extract features, segment the liver and detect tumors accurately. In this work, an optimized k-joint probabilistic segmentation-based ensemble classification model is proposed to address the issues of homogenous and heterogenous liver tumor detection. Additionally, novel image filtering, feature extraction and ranking approaches are proposed to improve the imbalanced liver tumor regions for classification process. The experimental results demonstrate that the proposed classification model based on k-joint probability segmentation has significantly improved the accuracy, recall, precision, and AUC compared to the existing models.
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