I-Brainer: Artificial intelligence/Internet of Things (AI/IoT)-Powered Detection of Brain Cancer
Abdullahi Umar Ibrahim, Ikedichukwu Onyemaucheya Nwaneri, Mercel Vubangsi, Fadi Al-TurjmanBackground/Objective:
Brain tumor is characterized by its aggressive nature and low survival rate and therefore, it is regarded as one of the deadliest diseases. Thus, misdiagnosis or miss-classification of brain tumors can lead to miss-treatment or incorrect treatment and reduce survival chances. Therefore, there is a need to develop a technique that can identify and detect brain tumors at early stages.
Methods:
Here, we proposed a framework titled I-Brainer which is an Artificial Intelligence/Internet of Things (AI/IoT)-powered classification of MRI into 4 classes. We employed a Br35H+SARTAJ brain MRI dataset which contains 7023 total images including no tumor, pituitary, meningioma, and glioma. To accurately classify MRI into 4 classes, we developed the LeNet model from scratch, and implemented 2 pre-trained models which include EfficientNet and ResNet-50 as well as feature extraction of these models coupled with 2 Machine Learning (ML) classifiers namely; k- Nearest Neighbours (KNN) and Support Vector Machine (SVM).
Results:
Evaluation and comparison of the performance of the 3 models have shown that ResNet-50 achieved the best result in terms of AUC (99%) and ResNet-50-KNN ranked higher in terms of accuracy (94%) on the testing set.
Conclusion:
This framework can be harnessed by patients residing in remote areas and as a confirmatory approach for medical experts.