Segmentation of brain tumors in Magnetic Resonance Imaging (MRI) has many applications in the diagnosis, treatment planning, and monitoring of the disease. The manual delineation of tumor sub-regions is time-consuming and can be influenced by inter-observer variability, requiring automated solutions. This study introduces an AI-based 3D brain tumor segmentation system based on a 3D U-Net architecture to segment brain tumors based on multi-classes with multi-modal MRI volumetric data. Four MRI modalities are processed, and the tumor regions are divided into edema, necrotic core, and enhancing tumor in the proposed model. To address class imbalance and enhance segmentation accuracy, a loss function based on the Dice coefficient is used for training. Besides segmentation, the system consists of quantitative tissue analysis via calculation of the relative distribution of tumor sub-regions and interactive visualization using a web-based dashboard. The experiments demonstrated successful volumetric segmentation and significant tissue quantification. The proposed framework emphasizes a system-level integration of segmentation, quantification, and visualization to enhance interpretability and practical usability.
D. U. Latha, M. Padma, D. Rajeshwari et al.· Engineering, Technology &...· 0 citations
Aquaculture is a vital element of global food security and economic sustainability. The changing environment from time to time and the rapid spread of diseases in aquaculture leading to huge economic losses, still pose a major challenge in the maintenance of healthy fish. Timely intervention for rapid and accurate fish health forecasting and diagnosis will decrease the fish mortality and increase the efficiency of fish production. In the current paper, a stacking model using heterogenous ensemble learning will be developed using three machine learning algorithms including the Random Forest algorithm, Support Vector Machine (SVM) and XGBoost together with Logistic Regression as the meta-classifier to predict fish health. Data preprocessing stage should be conducted prior to developing a model which will include missing value treatment, feature scaling, feature encoding and irrelevant feature elimination. The efficiency and generality of the proposed model will be estimated via using stratified 10-fold cross-validation technique. The quality of the model will be estimated by using the criteria including accuracy, precision, recall, F1 Score, confusion matrix and ROC curve analysis. In addition, SHapley Additive exPlanations will be applied to increase the explainability of the proposed model by showing the contribution of each environmental and biological factor into predicting fish health. The experimental results reveal that the heterogeneous stacking model can outperform the individual base learners and provide a transparent decision explanation. The framework is proposed to be utilized for intelligent fish health monitoring and contributing to sustainable aquaculture through predicting diseases in time and managing the aquaculture farms effectively, interpretable and scalable way.
Ranjan Kumar, Savita Choudhary· Journal of Intelligent Decis...· 0 citations
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