Brain Hemorrhage Detection in Medical Imaging

Brain hemorrhage exposure using DL/ML skill is a critical area in medical imaging, aiming diagnosing, treating life-threatening conditions. This paper explores various deep learning and machine learning approaches improve the accuracy/efficiency of brain hemorrhage exposure, utilizing both CT/MRI data. combination of techniques classification employed analyze complex medical images. Furthermore, hyperparameter tuning and regularization methods are applied to optimize model performance and reduce underfitting. Logistic r egression, decision trees, and ensemble models are tested for classification tasks, while feature engineering and data preprocessing techniques like standardization enhance model accuracy.