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

Abstract

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.

A robust approach for multi-type classification of brain tumor using deep feature fusion

“…The model achieved accuracy of 96.89 and 95.75% on dataset 1 and dataset 2, respectively. Hammad et al (2023) constructed a CNN model with 8 layers. The model achieved an accuracy of 99.48% for binary classification of brain tumors and 96.86% for three-class classification.…”

Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

“…Accuracy, precision, recall, and F1 score are important evaluation metrics extensively employed to assess the performance of a classification model [34]. The accuracy metric is calculated by dividing the overall count of accurately predicted observations in a given dataset by the total count of predictions generated.…”