Advancing Brain Tumor Detection: A Thorough Investigation of CNNs,
  Clustering, and SoftMax Classification in the Analysis of MRI Images
Jonayet Miah Duc M Cao Md Abu Sayed3 Md Siam Taluckder Md Sabbirul Haque Fuad Mahmud
Brain tumors pose a significant global health challenge due to their high
prevalence and mortality rates across all age groups. Detecting brain tumors at
an early stage is crucial for effective treatment and patient outcomes. This
study presents a comprehensive investigation into the use of Convolutional
Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance
Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy
individuals and patients with brain tumors, was processed and fed into the CNN
architecture. The SoftMax Fully Connected layer was employed to classify the
images, achieving an accuracy of 98%. To evaluate the CNN's performance, two
other classifiers, Radial Basis Function (RBF) and Decision Tree (DT), were
utilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The study
also introduced a clustering method for feature extraction, improving CNN's
accuracy. Sensitivity, Specificity, and Precision were employed alongside
accuracy to comprehensively evaluate the network's performance. Notably, the
SoftMax classifier demonstrated the highest accuracy among the categorizers,
achieving 99.52% accuracy on test data. The presented research contributes to
the growing field of deep learning in medical image analysis. The combination
of CNNs and MRI data offers a promising tool for accurately detecting brain
tumors, with potential implications for early diagnosis and improved patient
care.