Abstract
Brain tumors are a serious health issue that affects many people's lives. Such a tumor, which is either benign or malignant, can be fatal if malignant cells are not correctly diagnosed. According to the most recent human health care analysis system, the number of brain tumor patients has climbed dramatically and is now the 10th top cause of death. As a result, detecting brain tumors in their early stages can considerably improve the patient's prospects of complete recovery and therapy. Thanks to improvements in information and communication technology, the Internet of things (IoT) has reached an evolutionary stage in the development of the modern health care environment. This paper provides a detailed examination of brain tumor detection approaches. Moreover, two different scenarios for detecting brain tumors will be proposed. On one hand, the first scenario depends on applying a deep convolutional neural network directly to brain images. On the other hand, the second scenario presents an IoT-based framework that adopts a multiuser detection system by sending the images to the cloud for early detection of brain tumors, which makes the system accessible to anyone and anywhere for accurate brain tumor categorization. The proposed CNN structure can be considered a modified version of the pre-trained ResNet18 CNN. Additionally, two key hyper-parameters are used to fine-tune the OMRES model, firstly different optimizers are tested using different learning rates, batch sizes, and a constant number of epochs, and secondly, the impact of changing dropout rates is made. Finally, comparisons between the OMRES model and traditional pre-trained models are discussed. Based on simulation findings, the RMSProp algorithm with a dropout rate of 0.5 verifies the best outcomes over other algorithms, where the suggested model achieves superior improvement with the highest rated accuracy of 98.67% compared to the conventional CNNs.