Transformative Applications of AI in Biomedical Imaging

Abstract

Introduction: Brain tumors pose a significant danger to human health due to their complexity, variety and ability to progressive progress. Early and accurate diagnosis is important for effective treatment and improvement in survival rates. Traditional clinical processes are often dependent on time programs, subjective and expensive clinical equipment. The project presents an integrated, AI operated, multi-phase system for brain tumor detection and recommendations of personal treatment. By combining symptomatic evaluation, biomarker analysis, advanced imaging interpretation and machine learning models, this system aims to help health professionals with quick and more accurate clinical decisions.
Objectives: The main objective of this project is to develop an integrated, multi -phase system for early detection and personal treatment using a combination of clinical symptoms, molecular biomarkers, imaging and artificial intelligence. The system begins to evaluate patient -reported symptoms and risk factors to calculate a functional weighted risk index (FWRI) for initial risk assessment. It then analyzes a blood -based biomarker to generate a blood -based tumor signal index (BTSI), which provides insight at molecular level. Then it explains the ATR-FTIR and the Multi-Omics report through rule-based arguments for determining nonconformities. The MRI scan is then classified into tumors-types-glioma, meningioma, pituitary or no tumor-a fine- model VGG19 uses deep learning models. Finally, the system integrates the results of all previous stages to offer individual means based on tumor type, severity and general risk profile.
Methods: The proposed system follows a sequential five-stage methodology for comprehensive brain tumor detection and remedy making plans. In Stage 1, users input symptom severity and history through an interactive interface, which calculates a Functional Weighted Risk Index (FWRI) to assess preliminary tumor chance. Stage 2 entails the input of blood biomarker ranges, which might be analyzed to generate a Blood-Based Tumor Signal Index (BTSI), reflecting molecular signs of tumor presence. Stage 3 allows users to add ATR-FTIR or Multi-Omics reviews,that are interpreted using rule-primarily based logic to identify unusual styles that warrant imaging. In Stage 4, customers add brain MRI scans which can be processed through a pre-trained VGG19 convolutional neural network to classify the tumor into one among four classes: glioma, meningioma, pituitary, or no tumor. Finally, Stage five combines the outputs from all previous levels to generate customized treatment tips, taking into consideration the identified tumor type, severity, biomarker signals, and medical danger ratings
Results: The multi -phase system demonstrated promising consequences in all components. In phase 1, FWRI is effectively stable in categories with low, medium and high risks based on symptom input and severity. Phase 2 has shown that BTSI values have become close in line with a biomarker threshold installed, which strengthens the system's ability to reflect molecular tumor designs. Phase 3 explained ATR-FFIR and Multi-Omics data successfully, the exact flag of the unusual profile for further imaging analysis. In Step 4, VGG19 was based MR classification model accuracy of more than 92%, which distinguishes well between glioma, meningoom, pituitary tumor and no tumor. Finally, step 5 recommendations generated clinically relevant and similar relevant medical guidelines, which demonstrate the system's ability to provide integrated, computer-driven support for individual brain tumor care.
Conclusions: Finally, integrated five-phase brain tumor detection and treatment system effectively combine the evaluation of clinical symptoms, biomorker analysis, omics data interpretation, advanced MRI classification and individual treatment form into an integrated diagnostic pipeline. By taking advantage of rule -based arguments and deep teaching techniques, the system increases early identification accuracy, reduces clinical delays and supports data driven clinical decision -making. The sequential current ensures that each step does on the previous one, reduces false positive and optimizing patient -specific consequences. With further clinical verification, this AI-assisted framework has a strong ability to become a reliable and skilled tool in medical environments in the real world for early diagnosis and handling of brain tumors.