Deep Learning Approaches in Medical Image Segmentation: Implications for Brain Tumor Detection and Analysis

The procedure of image segmentation involves splitting images into distinct components to discern similarities or differences between regions. This facilitates the quantitative and/or qualitative analysis of lesions, thereby enhancing the reliability and accuracy of medical diagnoses. Traditionally, medical image segmentation was performed manually, slice by slice, requiring a high level of expertise to accurately define boundaries for individual areas. This manual process is time-consuming and error-prone. Currently, several deep learning methods have achieved significant advancements in image segmentation, surpassing the accuracy of traditional approaches. This study reviewed the effectiveness of deep learning models in accurately segmenting images of brain tumor patients. A search of the PubMed and Google Scholar databases, as well as the Asian Journal archives, was conducted to retrieve recent literature using the keywords: deep learning, Magnetic Resonance Imaging, image segmentation, and medical image processing. References from relevant literature were also reviewed to obtain additional sources. A critical and direct assessment of deep learning technologies on tumor MRI images was subsequently performed using these sources. The Dice scores served as metrics for evaluating the performance of the deep learning models. Based on the Dice scores, it can be inferred that deep learning models such as 3D FCNs, ResNet models, AGSE-VNet models, and encoder-decoder CNN architectures exhibit high segmentation accuracy in brain tumor images. The promising results demonstrated by deep learning-based segmentation approaches underscore their potential to enhance diagnostic capabilities in brain tumor detection and analysis.