Cysts are common in healthcare and can be associated with various diseases. They can develop in different body parts and contain fluid, semi-solid, or air. Brain cysts are masses that form in the brain, and surgical methods may be used to treat them. The importance of deep learning in medical imaging is steadily growing. This study attempted to detect brain cysts using various architectures, including Random Forest, Unet, AlexNet, and LeNet. The Random Forest algorithm was found to be more successful than the other algorithms. This algorithm is crucial for classification and regression problems as it trains a series of decision trees and consolidates their predictions to create a robust and powerful model. Magnetic resonance imaging was used to detect cysts. The accuracy rates for cyst detection were 79.52%, 89.99%, 90.41%, and 97.26%, respectively. Several models were employed for this purpose, including LeNet, AlexNet, Unet, and the Random Forest algorithm. The accuracy rates for cyst detection were 79.52%, 89.99%, 90.41%, and 97.26%, respectively.

This study focuses on the segmentation and characterization of arachnoid cysts in brain MRI images, aiming to enhance diagnostic accuracy through advanced image processing techniques. Arachnoid cysts are cerebrospinal fluid-filled sacs located between the brain or spinal cord and the arachnoid membrane. These cysts can be asymptomatic but may also cause neurological symptoms such as headaches, seizures, or cognitive impairments when they increase in size or pressure. Accurate detection and characterization are essential for timely intervention and treatment. In this research, 269 brain MRI images were analyzed using connected component analysis (CCA) and contrast-limited adaptive histogram equalization (CLAHE). CLAHE was employed to enhance image contrast, particularly in regions with subtle intensity differences, while CCA facilitated the segmentation of connected regions corresponding to cysts. The smallest connected components were identified and analyzed to isolate arachnoid cysts with high precision. Post-segmentation, quantitative analysis was performed to extract features such as size, shape, and density, enabling comprehensive cyst characterization. Additionally, calculations for area and approximate volume were conducted, providing critical information for clinical assessment. Visual validation of segmentation outcomes confirmed the effectiveness of the applied methods in accurately delineating cyst boundaries. This research addresses a significant gap in the existing literature. While most studies focus on brain tumor segmentation, there is limited work on arachnoid cyst detection and volume estimation. By integrating image processing techniques tailored for arachnoid cysts, this study offers a novel approach to their diagnosis and monitoring. The findings demonstrate the potential for automated diagnostic tools, reducing subjectivity and improving efficiency in clinical workflows. The proposed methodology aligns with advancements in medical imaging and contributes to the development of improved tools for neuroimaging diagnostics, paving the way for more precise and reliable assessments in the detection of brain pathologies.

Cysts are sacs filled with fluid that can form in various organs, such as the kidneys, liver, breast, and brain. Treatment of these sacs may require surgical intervention. The importance of machine learning in detecting abnormal tissues in medical imaging is increasingly evident. This study specifically focuses on using deep learning structures to detect arachnoid cysts in the brain. The study employed Logistic Regression, InceptionV3, Kernel DVM, and Googlenet algorithms to detect arachnoid cysts. The accuracy rates achieved were 98.63% for Logistic Regression, 92.83% for InceptionV3, 92.38% for Kernel DVM, and 91.42% for Googlenet. Logistic Regression was the most successful algorithm. The study utilized data obtained from a 1.5T GE Magnetic Resonance Imaging (MRI) device.