An Effective System for Brain Pathology Classification using Hybrid Deep Learning

Abstract

Brain tumours are among the most serious and life-threatening health conditions, with their prevalence steadily increasing worldwide. Early detection and accurate classification play a crucial role in determining appropriate treatment strategies, significantly improving the chances of patient survival. However, brain tumour classification remains a challenging task due to the complex nature of tumour structures, which can vary greatly in size, shape, and location. Conventional methods, while effective to some extent, often struggle to achieve the desired accuracy, leading to potential misdiagnosis or delayed treatment. To address these challenges, this paper presents a novel and effective system for brain tumour classification using a hybrid deep learning algorithm. The proposed model integrates Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to leverage the strengths of both architectures. The CNN is employed to extract crucial spatial features from MRI scan images, capturing intricate patterns and textures that are essential for identifying tumours. Meanwhile, the RNN component is designed to analyze sequential dependencies in the extracted features, enabling the model to better understand the spatial relationships within the medical images. Through extensive experimentation and performance evaluation, the hybrid model demonstrated superior classification accuracy compared to traditional methods. The results highlight the system’s ability to minimize false positives and improve overall precision and recall. This enhanced performance indicates that the proposed hybrid deep learning algorithm has strong potential to support healthcare professionals in making faster, more reliable diagnostic decisions, ultimately contributing to improved patient outcomes.