Title : Advancing brain tumor classification through deep learning techniques: Insights from VGG-16 ensembling and ResNet50
Abstract:
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest?performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1?score = 91.78%,92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work.
Audience Take Away Notes:
- Understanding the potential of deep learning in brain tumor detection: The audience will gain insights into how advanced deep learning techniques, such as CNNs and the VGG-16 architecture, can be used to improve the accuracy and efficiency of brain tumor detection and classification.
- Application of VGG-16 ensembling in tumor region classification: Attendees will learn about the innovative approach of using VGG-16 ensembling for generating convolutional feature maps and classifying tumor regions in MR images, which could be applied to their own research or work in medical imaging.
- Importance of early disease detection and precise decision-making: Participants will understand the significance of early detection of brain tumors using reliable A.I. and neural network classification algorithms, which could help them make more informed decisions in their clinical practice or research.
- Comparison of deep learning techniques with conventional approaches: The audience will learn about the advantages of deep learning techniques in terms of accuracy and efficiency compared to traditional methods for detecting brain tumors, enabling them to consider adopting similar techniques in their work.
- Future directions in brain tumor detection research: By understanding the study's recommendations for future research, the audience will be able to identify potential areas of focus and collaboration in the field of brain tumor detection using deep learning techniques.
- Researchers and academics can apply the deep learning techniques presented in their own studies, expanding the scope of their research and potentially improving the accuracy of brain tumor detection and classification.
- Clinicians and healthcare professionals can gain insights into the benefits of using deep learning techniques for diagnosing and treating brain tumors, leading to better patient outcomes and more efficient decision-making.
- Designers and engineers working in medical imaging can apply the presented techniques to develop innovative solutions and technologies for detecting brain tumors, ultimately simplifying and improving the efficiency of their work.
