AI-Based Tumor Identification with Medical Image Segmentation

Abstract

Medical image analysis has emerged as a significant area of research due to the growing demand for early and accurate detection of cancerous tumors. Identifying tumors from medical images is a challenging task because tumors can exhibit considerable variations in shape, size, location, texture, and intensity. Traditional manual examination of medical scans is often timeconsuming and largely dependent on the expertise and experience of radiologists. Therefore, there is an increasing need for intelligent Computer-Aided Diagnosis (CAD) systems that can support healthcare professionals by improving the accuracy, efficiency, and consistency of tumor detection. This project proposes an AI-based tumor identification and classification system using advanced medical image segmentation and deep-learning techniques. The proposed framework is designed to process medical images obtained from different imaging modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-ray scans. Initially, the input medical images undergo a series of preprocessing operations, including image resizing, normalization, noise removal, and contrast enhancement, to improve image quality and prepare the data for further analysis.Following preprocessing, the enhanced medical images are processed using a U-Net deep-learning architecture to perform the initial segmentation of suspected tumor regions. The segmented output is subsequently refined using the DeepLabV3+ model, which utilizes multi- scale contextual information to improve segmentation accuracy and precisely identify tumor boundaries. After segmentation, a Convolutional Neural Network (CNN) is employed to extract important spatial and visual features from the identified tumor regions. Based on these extracted features, the system classifies the detected tumor into benign or malignant categories, providing valuable information to support clinical diagnosis and decision-making. The proposed system demonstrates the potential of Artificial Intelligence and deep learning to enhance medical image analysis by enabling faster, more accurate, and consistent tumor identification and classification. By combining preprocessing, U-Net-based segmentation, DeepLabV3+ boundary refinement, and CNNbased classification within a unified framework, the system aims to reduce the workload of medical professionals while improving diagnostic reliability. Furthermore, the modular architecture of the proposed system provides opportunities for future enhancements, including multi-class tumor classification, integration with hospital information systems, cloud-based deployment, real-time diagnostic support, and the incorporation of transformer-based deep-learning models. Such advancements can further strengthen the role of AI-assisted diagnostic systems as supportive tools for radiologists and healthcare professionals in modern clinical environments.