Year: 2024
Type: article
Abstract: Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have been extensively applied to image recognition and classification tasks, achieving significant success in medical image analysis. Radiologists face challenges in accurately diagnosing brain tumours due to the variety of tumour cells. Recently, computer-aided diagnostic methods using magnetic resonance imaging (MRI) have been developed to aid in brain cancer diagnosis. CNNs play a crucial role in medical image analysis, including the detection of brain cancers, helping physicians overcome the difficulties in identifying brain tumours, especially in the early stages of brain haemorrhage. The proposed model categorizes brain images into four classes: Normal, Glioma, Meningioma, and Pituitary. It achieves a recall of 95%, an accuracy of 95.44%, and an F1-score of 95.36%.
DOI: 10.29070/62qdeh70
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4402238703