Dr Anusha Nalajala and Dr Anita Inturi from the Department of Computer Science and Engineering have jointly authored a research paper titled “Decoding Brain Tumors: Comprehensive Insights into Detection and Evaluation Approaches,”published in Engineering. The paper presents an in-depth exploration of modern computational and analytical techniques used in brain tumor detection, diagnosis, and assessment, offering an integrated perspective on current methodologies and emerging advancements in the field. Their work contributes to strengthening the intersection of healthcare and technology, supporting improved medical decision-making and future innovation in brain tumor analysis.

Brief Abstract:

This study reviews recent advances in artificial intelligence techniques for brain tumor detection, classification, and segmentation using MRI data. It compares machine learning, deep learning, transformer-based, and hybrid models, highlighting their clinical relevance and limitations. The work identifies key challenges such as data scarcity, interpretability, and generalization across institutions. It also discusses emerging trends including explainable AI, multimodal learning, and federated approaches. Overall, the study offers guidance for developing reliable and clinically deployable AI-based diagnostic systems.

Explanation of the Research in layperson’s terms:

Brain tumors are difficult to detect and analyze accurately, and doctors often rely on manual examination of MRI scans, which can be time-consuming and subjective. This research explains how artificial intelligence (AI) can help doctors by automatically identifying, classifying, and outlining brain tumors from MRI images. It reviews different AI techniques and explains which ones work well, which have limitations, and why some are not yet ready for routine hospital use. The study also highlights challenges such as limited medical data, lack of transparency in AI decisions, and differences between hospitals. Overall, the work helps guide the development of safer, more reliable AI tools that can support doctors in diagnosing brain tumors more accurately and efficiently.

Practical implementation and Social Implications:

The findings of this research can help hospitals and doctors use artificial intelligence tools to support faster and more accurate diagnosis of brain tumors from MRI scans. In practical terms, AI-based systems reviewed in this study can assist radiologists by highlighting suspicious tumor regions, reducing manual effort, and improving consistency in diagnosis. This is especially useful in busy hospitals or regions with a shortage of experienced specialists.

From a social perspective, such technology has the potential to improve early detection of brain tumors, leading to timely treatment and better patient outcomes. It can also help reduce diagnostic errors and provide more equal access to quality healthcare, particularly in rural or low-resource settings. By addressing challenges like reliability, transparency, and data privacy, this research supports the responsible adoption of AI in healthcare, ultimately contributing to safer, more affordable, and more efficient medical services.

Collaborations:

This research was conducted through a collaborative effort between SRM University–AP, India, and the University of the Witwatersrand, South Africa. The collaboration brought together expertise in computer science, machine learning, and medical image analysis, enabling a comprehensive and interdisciplinary review of AI-based brain tumor detection techniques. The international partnership strengthened the study by integrating diverse academic perspectives and aligning the research with global trends in artificial intelligence and healthcare applications.

Future Research Plans

Building on this work, future research will focus on developing clinically deployable AI models for brain tumor analysis that are robust, interpretable, and generalizable across hospitals. Planned directions include exploring federated learning to enable multi-institutional collaboration while preserving patient privacy, and integrating explainable AI techniques to improve clinician trust in automated predictions. The research will also investigate multimodal frameworks that combine MRI with clinical and genomic data for improved diagnosis and prognosis. Emphasis will be placed on external validation, real-world testing, and alignment with regulatory and ethical requirements to support translation into routine clinical practice

Link to the article –  https://doi.org/10.1002/eng2.70524.