2023, Vol. 4, Issue 1, Part A

This research paper proposes a brain tumor detection system using neural networks. The authors use a dataset of Brain MRI Images for Brain Tumor Detection obtained from Kaggle.com and compare the performance of two models of Convolutional Neural Network (CNN). The first model is a simple CNN, and the second model is a model of hybrid deep learning Long-Short-Term Memory in Convolutional Neural Networks (CNN-LSTM). The experiments show that the CNN-LSTM model outperforms the simple CNN model in terms of accuracy, sensitivity, and specificity. The proposed system achieves high accuracy and can be used for accurate and efficient brain tumor detection.

Cancer detection is a crucial task in the field of the health imaging. Traditional methods of detecting brain tumors are time-consuming and require a lot of expertise. With the advent of deep learning and neural networks, the detection of brain tumors has become more efficient and accurate.

The use of neural networks for brain tumor detection has the potential to revolutionize the field of medical imaging and improve patient outcomes.