GUI-Integrated Advanced Intelligent Framework for Diagnosis of Bone Cancer

This paper aims to develop a Graphic User Interface (GUI) that analyzes and detects bone cancer from input images, enhancing diagnostic efficiency. The primary research question focuses on whether an automated, image-processing-based system can accurately identify and segment cancerous regions in bone images using color-based segmentation and clustering techniques. The system employs a multi-stage image processing pipeline to detect bone cancer. The process begins with the binarization of images, followed by the selection of terahertz images for analysis. The images undergo diffusion filtering, which is then followed by morphological operations to extract relevant features. K-means clustering is applied to segment the cancerous regions, and the resulting visualizations aid in identifying carcinogenic cells based on region analysis. Results: The developed GUI, integrated with MATLAB’s image processing tools, streamlines the workflow, providing a time-efficient and automated method for detecting bone cancer. The color-based segmentation and clustering techniques effectively identify cancerous areas within terahertz images, facilitating improved visualization and analysis. Conclusion: The proposed GUI, guided by image processing algorithms in MATLAB, offers a reliable and efficient solution for bone cancer detection. The system's automated nature enhances the speed of diagnosis, offering significant potential for use in medical imaging, with a focus on accuracy and time efficiency in cancer detection t)