Integrating interpolation techniques with deep learning for accurate brain tumor classification

Artificial Intelligence (AI)-powered Computer vision techniques have revolutionized Medical Image Analysis (MIA), enabling accurate detection, diagnosis, and treatment of various disorders such as brain tumors. Brain tumors are a worldwide primary health concern that affects thousands of people. Precisely identifying and diagnosing brain tumors is vital for effective management and life expectancy. Current advances in AI, particularly in Deep Learning (DL) methods have shown immense possibilities to analyze medical images, including MRI. However, the quality of the MRI images significantly impact the overall accuracy of the classification framework. To tackle this issue, we investigated the effect of various Interpolation Techniques (IT) on enhancing Magnetic Resonance Imaging (MRI) image quality, including Nearest Neighbour IT, Bilinear IT, Bicubic IT, and Lanczos IT. Furthermore, we employed Transfer Learning to leverage pre-trained Convolutional Neural Networks (CNNs) architectures, specifically DenseNet201. We proposed a modified DenseNet201 model by adding additional layers and extracting features from the interpolated brain MRI images. We used two publicly available brain tumor datasets. Our experimental results illustrated that the combination of Lanczos IT and fine-tuned DenseNet201 attained the highest accuracy of 99.21% and 99.60% in Dataset-1 and Dataset-2, respectively, for brain tumor classification. Our analysis highlights the importance of image interpolation techniques in improving medical image quality and ultimately improving diagnostic accuracy. Our findings have significant implications for the development of AI-powered decision support systems in medical imaging, enabling healthcare professionals to make more accurate diagnoses and informed treatment decisions.