​Dr. Sanjalawe and Prof. Al-Sayeed actively participated in the 5th Conference on Computer and Information Sciences (ISCIS2024) by presenting their paper titled "Smart Tumor Detection: Unleashing AI for Superior Accuracy and Early Diagnosis."

Abstract: 

This paper presents a comprehensive AI-based framework for enhancing tumor detection accuracy and efficiency, leveraging advanced deep learning models such as U-Net and 3D U-Net. Using the BraTS 2021 dataset, which includes annotated multimodal MRI scans, the proposed model effectively segments and classifies tumor regions. By integrating transfer learning and Grad-CAM for model interpretability, this study addresses key challenges in clinical tumor detection, including diagnostic accuracy and transparency of AI decision-making. Experimental results demonstrate the model's superior performance in terms of accuracy, Dice Similarity Coefficient (DSC), sensitivity, specificity, and precision when compared to state-of-the-art methods. The research also explores the potential for real-time analysis and personalized treatment, offering insights into how AI can be integrated into clinical workflows for improved patient outcomes in cancer treatment.​