Narrow Results

Diagnosing brain tumors is particularly difficult because they can grow in unpredictable ways and look very different on MRI scans. The current methods used to automatically identify these tumors often struggle because of the wide variety of tumor types and the complex structure of the brain. As a result, these methods don’t always classify tumors accurately, which can affect patient treatment and outcomes. The main problems with these methods are that they find it hard to distinguish between different types of tumors accurately and to deal with the various ways tumors can appear on MRI scans. To improve this situation, our study integrates the robust image classification capabilities of VGG19 with the sequential data processing strengths of LSTM. This synergistic approach enhances our model’s ability to accurately classify various types of brain tumors from MRI scans, addressing the inherent challenges associated with tumor heterogeneity in medical imaging. VGG19, a deep convolutional neural network, is employed to extract detailed features from MRI scans, facilitating precise tumor characterization based on visual patterns and LSTM complements VGG19 by capturing temporal dependencies in the sequential data of MRI scans, enabling the model to discern subtle variations in tumor appearances over time. By leveraging the combined power of VGG19 and LSTM architectures, our study achieves significant advancements in the accurate classification of brain tumors from MRI images. This approach not only enhances diagnostic precision but also lays the groundwork for future improvements in neuro-oncological imaging diagnostics. Our study includes 1000 patients evaluated with MRI for brain tumors. We achieved an overall accuracy of 98.32% demonstrating the efficacy of our VGG19-LSTM model in accurate tumor classification. By using both, our model aims to get better at understanding MRI scans and, as a result, be more accurate at identifying brain tumors. This combination is a new step forward in making brain tumor diagnosis more precise through a detailed and cooperative approach using neural networks.

The following topics are under this section:

• Life of a Clinician-Scientist at the National Neuroscience Institute, Singapore

For the month of August 2021, APBN looks at some of the progress made in cancer research. In Features, we have Yie Hou Lee and Michael Birnbaum from the Singapore-MIT Alliance for Research and Technology Critical Analytics for Manufacturing Personalized-Medicine (SMART-CAMP) to share about the future of CAR T cell manufacturing. Next, a team of researchers from the National Neuroscience Institute, National University of Singapore, and the Duke-NUS Medical School enlightens us on the difficulty of treating glioblastoma brain tumours and how they plan to address its critical issues. Then we have Dr. Chi-Jui Liu and Hsiao Yun Lu to talk about hereditary cancers and how we may improve our odds in this game of roulette. In Columns, we have an analysis by Dr. Ping-Chung Leung on the integrative use of Traditional Chinese Medicine in managing treatment outcomes of COVID-19 patients and a reflection by Dr. Chris Nave on the lessons we can take away from the development of COVID-19 vaccines. Finally, in Spotlights, we share highlights from the Vaccines World Summit 2021 and an interview with Mr. Abel Ang, Group Chief Executive of Advanced MedTech on how their new venture AbAsia Biolabs can help meet Singapore’s need for increased COVID-19 test kits as we enter a new normal.