Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and ResNet50.

Accurate classification of brain tumors in medical images is vital for effective diagnosis and treatment planning, which improves the patient's survival rate. In this paper, we investigate the application of Convolutional Neural Networks (CNN) as a powerful tool for enhancing diagnostic accuracy using a Magnetic Resonance Imaging (MRI) dataset. This study investigates the application of CNNs for brain tumor classification using a dataset of Magnetic Resonance Imaging (MRI) with a resolution of 200 × 200 × 1.

Correlation between blood parameters in the early and later stages of pregnancy: A retrospective study.

Objective: Anemia is common in pregnant women and is associated with various maternal and fetal complications. However, the effect of fluctuations in hemoglobin levels during pregnancy on birth outcomes remains unclear. Therefore, we investigated the association between maternal hemoglobin levels at different stages of pregnancy and delivery outcomes.