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Corresponding Author ORCID Identifer

Sangeeta Girihttps://orcid.org/0009-0001-5213-4786

Section

Regular Issue

Abstract

The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can have a wide range of sizes, shapes, and types, conventional diagnostic techniques like MRI interpretation and manual evaluations are difficult and time-consuming. Traditional methods frequently depend on human expertise, which is prone to errors, delays, and variability. Deep learning (DL) developments, on the other hand, have completely changed this field by providing increased automation, efficiency, and precision in tumour detection and classification because they can automatically extract pertinent features from MRI scans, Convolutional Neural Networks (CNNs) have shown impressive success in medical image analysis in recent years. CNNs improve the classification of tumour types like gliomas, meningiomas, and pituitary tumours by using multiple layers to find patterns in imaging data. Despite their efficiency, CNNs sometimes struggle with complex tumour patterns, requiring further enhancement in feature extraction. Vision Transformers (ViTs) have become a viable substitute to overcome this constraint. ViTs are especially good at identifying complex tumour structures because, in contrast to CNNs, they use self-attention mechanisms to capture global image dependencies. ViTs can perform better diagnostics by more thoroughly analysing entire MRI images. Additionally, hybrid methods that combine CNNs and ViTs have demonstrated better outcomes, taking advantage of both long-range spatial understanding (ViTs) and local feature extraction (CNNs). These developments allow for real-time medical applications, drastically improve diagnostic accuracy, and lower false positives. Neuro-oncology could undergo a revolution with the incorporation of DL models into clinical workflows, which would improve tumour detection's accuracy, speed, and accessibility. These techniques will be further developed in future studies, guaranteeing even higher accuracy and versatility in medical imaging.

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