Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model.

Simple Summary: This paper discusses the importance of early detection of brain tumors and the limitations of traditional diagnosis methods. The use of deep learning models for brain tumor detection is introduced as a potential solution, but the high computing costs and potential biases in training data pose challenges. The study proposes a new, end-to-end, lightweight deep learning model for brain tumor detection that outperforms other models and is suitable for real-time applications. The study also provides a framework for secure data transfer of medical lab results and security recommendations to ensure security on the Internet of Medical Things (IoMT). In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular deep learning technique for brain tumor detection because they can be trained on vast medical imaging datasets to recognize cancers in new images. Despite its benefits, which include greater accuracy and efficiency, deep learning has disadvantages, such as high computing costs and the possibility of skewed findings due to inadequate training data. Further study is needed to fully understand the potential and limitations of deep learning in brain tumor detection in the IoMT and to overcome the obstacles associated with real-world implementation. In this study, we propose a new CNN-based deep learning model for brain tumor detection. The suggested model is an end-to-end model, which reduces the system's complexity in comparison to earlier deep learning models. In addition, our model is lightweight, as it is built from a small number of layers compared to other previous models, which makes the model suitable for real-time applications. The optimistic findings of a rapid increase in accuracy (99.48% for binary class and 96.86% for multi-class) demonstrate that the new framework model has excelled in the competition. This study demonstrates that the suggested deep model outperforms other CNNs for detecting brain tumors. Additionally, the study provides a framework for secure data transfer of medical lab results with security recommendations to ensure security in the IoMT. [ABSTRACT FROM AUTHOR]