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Brain tumors pose a significant global health challenge due to their highprevalence and mortality rates across all age groups. Detecting brain tumors atan early stage is crucial for effective treatment and patient outcomes. Thisstudy presents a comprehensive investigation into the use of ConvolutionalNeural Networks (CNNs) for brain tumor detection using Magnetic ResonanceImaging (MRI) images. The dataset, consisting of MRI scans from both healthyindividuals and patients with brain tumors, was processed and fed into the CNNarchitecture. The SoftMax Fully Connected layer was employed to classify theimages, achieving an accuracy of 98%. To evaluate the CNN's performance, twoother classifiers, Radial Basis Function (RBF) and Decision Tree (DT), wereutilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The studyalso introduced a clustering method for feature extraction, improving CNN'saccuracy. Sensitivity, Specificity, and Precision were employed alongsideaccuracy to comprehensively evaluate the network's performance. Notably, theSoftMax classifier demonstrated the highest accuracy among the categorizers,achieving 99.52% accuracy on test data. The presented research contributes tothe growing field of deep learning in medical image analysis. The combinationof CNNs and MRI data offers a promising tool for accurately detecting braintumors, with potential implications for early diagnosis and improved patientcare.

Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images