Advancing medical images-based tumor detection and classification A comprehensive framework utilizing image processing and artificial intelligence with MATLAB GUI Integration

Abstract

In modern medical practice, precise detection and classification of tumors are critical for early diagnosis and effective treatment planning. Despite advancements in medical imaging, challenges persist in accurately identifying subtle abnormalities in early stages, often leading to delays in treatment. Our research addresses these challenges by leveraging advanced image processing techniques and artificial intelligence (AI) algorithms. Using Magnetic Resonance Imaging (MRI) as a non-invasive diagnostic tool, our framework focuses on automating the segmentation of abnormal tissues. We have developed and implemented a comprehensive methodology using MATLAB, incorporating Gaussian filtering for pre-processing to enhance image quality and reduce noise. Subsequently, image segmentation was performed using thresholding techniques. For feature extraction, we utilized Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Gray-Level Co-occurrence Matrix (GLCM). These features were then fed into a Support Vector Machine (SVM) for accurate tumor detection and classification, distinguishing between benign and malignant tumors. Our study includes the creation of a robust local database of MRI scans and demonstrates the adaptability of our framework across various organ systems beyond brain tumors. Through extensive experimentation and validation, we showcase the effectiveness of our approach in real-world clinical scenarios, contributing to enhanced medical imaging capabilities and improved patient outcomes