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The brain is a vital and complex organ of the human system. It is responsible for controlling the overall activity of the human body. Normal functionality of the organs depends on the healthy brain and any disorder in the brain may cause a critical and life-threatening condition. There are several disorders related to the brain like Alzheimer's, Tumors, Cancer, Epilepsy, and Stroke. The severity of the brain disorder depends on the region and its intensity. Among all these disorder brain Tumors is the most common which may occur in any age and gender. There are several modalities used to acquire the image of the brain to diagnose a brain tumor but identifying the tumor from the image requires profound knowledge and expertise. Physically identification of the tumor from the radiologic image is a common practice of medical professionals; however, the procedure has the probability of human error. Computer-aided diagnoses of Brain Tumors assist the medical professional to estimate the proper region of the tumor in an image in a better way. Several approaches have been adopted by the researcher to identify the brain tumor from a radiologic image. This article proposed a MATLAB-based graphical user interface to assist medical professionals to estimate Brain Tumors from radiologic Images. The algorithm is based on the acquisition of the radiological image, preprocessing, Image segmentation, and algorithm implementation for Tumor detection. Further, the Convolutional Neural Network techniques have been applied to compare the accuracy of the six different pre-trained models. Significant results have been achieved via the proposed algorithm. The algorithm reduces the process time and rate of human error cost-effectively. The ResNET50 Approach-1 is identified as the better approach among the six approaches with a Training and Validation Accuracy of 99 % and Test Accuracy of 93%. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation plays a vital role in medical image analysis, aiding in accurate diagnosis and treatment planning. This research article proposes a novel approach for brain tumor segmentation utilizing the K-Means clustering algorithm and Fuzzy C-Means (FCM) clustering technique. The K-means segmentation is initially used to cluster the brain image into distinct regions based on intensity values. Subsequently, the FCM algorithm is applied to further refine the segmentation results by considering the fuzzy memberships of pixels within each cluster. The combination of K-means segmentation and FCM algorithm improved the tumor detection accuracy and also reduced computational complexity compared to traditional methods. The objective is to enhance the accuracy and efficiency of tumor segmentation in magnetic resonance imaging (MRI) scans. [ABSTRACT FROM AUTHOR]