Advancements in Automated Tumor Detection Screening: A Comprehensive Review and Comparative Analysis

This paper offers a thorough examination and comparative assessment of recent advancements in automated tumor detection methodologies. Covering a range of aspects related to tumor detection, such as image capture, preprocessing, feature extraction, classification methods, and performance assessment measures, the review delves into the application of multiple medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and histopathological photography. Additionally, it looks at how deep learning and machine learning algorithms— specifically, support vector machines (SVMs), convolutional neural networks (CNNs), and ensemble learning techniques—achieve accurate tumor categorization. Moreover, the paper addresses challenges and limitations inherent in automated tumor detection systems, including data imbalance, interpretability of machine learning models, and integration with clinical workflows. By conducting a comparative analysis of existing automated tumor detection approaches, this paper aims to provide researchers and clinicians with valuable insights into current state-of-the-art techniques, their strengths, and limitations. Furthermore, it identifies promising avenues for future research to advance the field of automated tumor detection screening and ultimately improve patient outcomes in oncology.