Malignant brain tumors are generally classified to be extremely aggressive and often can be fatal when not met with immediate action. Glioblastoma Multiforme is the most common type of malignant tumor found in the brain and is extremely aggressive. For this reason, advanced detection of malignant brain tumors is necessary for optimal mitigation. Conversely, the classification of tumors during Medical Resonance Imaging can be difficult due to bodily movements resulting in the movement of the tumor. The movement of the tumor can disrupt targeted radiotherapy and can also, at times, result in treatments about radiotherapy damaging healthy areas of the brain rather than areas of the tumor. This study proposes a novel deep learning system that can identify tumors from MRI images; which can be helpful for the case of early detection, as well as being able to track tumors during active imaging; resulting in higher efficiency with targeted radiotherapy. This is done utilizing Convolutional Neural Networks (CNNs) created via deep learning frameworks. With the image classification of tumors; 97% accuracy was achieved with optimization. The tumor-classification deep learning system achieved an accuracy of 98%. Further testing is required for optimization; with this optimization, higher accuracy can be reached.

The authors have declared no competing interest.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study used ONLY openly available human data that were originally located at the cancer imaging archive.

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All data produced in the present study are available upon reasonable request to the authors All data produced in the present work are contained in the manuscript