BRAIN-SCN-PRO: A machine learning model for the classification of brain tumors using a convolutional neural network architecture

Accurate detection and classification of brain tumors from MRI images are pivotal in IoT healthcare systems, enabling early diagnosis and tailored treatment strategies. Deep learning algorithms, specifically Convolutional Neural Networks (CNNs), have demonstrated significant potential for enhancing the accuracy of Computer-Aided Diagnostic Systems (CADS) for brain tumor identification. This study aimed to develop a CNN-based machine learning model trained on a comprehensive dataset comprising 17,000 T1-weighted contrast-enhanced MRI scans to achieve precise and reliable classification of various brain tumor types. The experimental results demonstrated the remarkable capabilities of the proposed model, with an impressive classification accuracy of 99.37 %. This high level of accuracy suggests that the proposed model has the potential to become a decision support system for radiologists, aiding them in making swift and accurate diagnoses, as well as formulating tailored treatment regimens for patients. This study represents a significant step forward in the realm of IoT healthcare systems, offering a highly accurate and easily accessible solution for brain-tumor classification. The application of BRAIN-SCN-PRO in clinical practice has the potential to revolutionize early detection and management of brain tumors, ultimately improving patient outcomes and quality of life. The model has been made available as an Android mobile application called BRAIN-SCN-PRO on Google Play Store. ( https://play.google.com/store/apps/details?id=com.ap360.brscn ).