Agnostic multimodal brain anomalies detection using a novel single-structured framework for better patient diagnosis and therapeutic planning in clinical oncology

Abstract

The application of image processing in medical image analysis offers physicians numerous advantages in diagnosing and predicting patient recovery. A typical task that requires sensitive handling is the detection of a cerebral aneurysm, which generally involves the arteries in the human brain. Due to peculiar and complicated tissue structures, such as white and grey matter, which make up most of the brain, it is indeed challenging to visualize the aneurysm and the affected parts. Magnetic resonance angiography (MRA) is a tool for such a process, and it is difficult for radiologists and oncologists to decide. To overcome these drawbacks, the researchers proposed a novel algorithm named artificial bee colony optimization (ABC) with spatially constrained adaptively regularized kernel function-based fuzzy C-means (ABC-ScARKFCM) in this work. The system outperforms the conventional fuzzy C-means clustering method (FCM), which has inaccuracies in intensity handling and segmentation, and a poor convergence rate. The developed algorithm performed well on clinical MRA and Magnetic Resonance images (MRI) from the BraTS challenge dataset (2013, 2015, 2018, 2019, 2020 and 2021). The algorithm achieved dice score, sensitivity and specificity of 87.89%, 98.9% and 98.98%, respectively, which is very remarkable and shows that the applicability of the algorithm can be extended to oncology applications, where suppression of openness/anonymity is expected in diagnosis and assessment of prognosis of patients after therapy.