Evaluating VGG-16 Performance in Brain Tumor Detection: A Comprehensive Review - Fahim, Sajid Faysal; Mollah, Nayem; Sultana, Nusrat; Khan, Md. Mohshiu Islam (International Journal of Computer Sciences and Engineering, 2023)
This comprehensive examination deeply explores the evaluation of the VGG-16 architecture in the critical and significant domain of brain tumor detection, which holds utmost importance in the field of medical image analysis. The study meticulously and thoroughly evaluates the strengths and weaknesses of the VGG-16 model, taking into account its pivotal role as a deep learning model specifically crafted for this crucial medical application A comprehensive and meticulous evaluation is conducted to offer a thorough and all-encompassing assessment of the effectiveness of VGG-16 in precisely identifying brain tumors. This entails a meticulous and detailed exploration of diverse datasets, methodologies, and benchmarking metrics. The significant findings obtained from this extensive analysis shed crucial light on the immense potential of the VGG-16 model in the field of brain tumor detection, while also highlighting its inherent limitations and areas that could be enhanced. These invaluable observations have been demonstrated to be extremely advantageous for both individuals conducting research and professionals working in the field of medical image analysis. It is of utmost significance to acknowledge that this analysis ultimately underscores the crucial significance of continuous research initiatives directed towards enhancing the efficacy of VGG-16 specifically in the domain of brain tumor detection. The ultimate objective of these endeavors is to formulate healthcare solutions that are more precise and efficient, thereby greatly benefiting patients requiring such interventions.