A Deep Learning Model-Based Approach for Brain Tumor Detection in Low-Brightness and Low-Contrast MRI

Magnetic resonance imaging (MRI) has become a valuable diagnostic assessment means for the detection, segmentation, and characterization of brain tumors. However, low brightness and low contrast in MRI images pose a significant challenge for accurate tumor detection, especially in the early stages. Several approaches have been proposed to address this challenge, including image enhancement and filtering techniques. However, these methods often result in loss of image details, making it difficult to discern the tumor regions from the non-tumor ones. To overcome these limitations, deep learning-based approaches have gathered attention in recent years for their capability to automatically learn features from the input images and achieve high accuracy in various medical imaging tasks. The aim of our research is to present a deep learning-based methodology for detecting brain tumors in low-brightness and low-contrast MRI images. We employ a neural network with convolutions’ (CNN) architecture, which has been proven to be effective in acquiring complex image features. Previous studies have used deep learning techniques for brain tumor segmentation and detection (Ramin Ranjbarzadeh et al. in Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images [1]). However, these studies did not specifically address the problem of low brightness and low contrast in MRI images. In contrast, our proposed method is designed to capture the subtle differences between tumor regions and non-tumor regions in such MRI images. Our CNN model has been trained and validated on a larger dataset of MRI images, including both normal and tumor-containing images. Our results demonstrate that our proposed method achieves high accuracy and specificity in detecting brain tumors, even in low-brightness and low-contrast MRI images. Additionally, our method has the potential to aid healthcare professionals in precisely and promptly pronouncing tumors in brain, resulting in better patient results. To sum up, our proposed approach, which is based on learning from images more deeply, has the potential to identify brain tumors in MRI images with low brightness and contrast with a promise of high accuracy. This approach could be incorporated into clinical processes, improving the detection accuracy thereby saving patient lives.