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Enhancing Medical Diagnosis Through Multimodal Medical Image Fusion
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Author(s): Kashi Sai Prasad (Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India), Meghana Kolli (Department of Computer Science and Engineering - Artificial Intelligence and Machine Learning, MLR Institute of Technology, Hyderabad, India), Bhargavi Linga (Department of Computer Science and Engineering - Artificial Intelligence and Machine Learning, MLR Institute of Technology, Hyderabad, India), Sai Shreeya Chikati (Department of Computer Science and Engineering - Artificial Intelligence and Machine Learning, MLR Institute of Technology, Hyderabad, India)and Tiruneswar Veeranki (Department of Computer Science and Engineering - Artificial Intelligence and Machine Learning, MLR Institute of Technology, Hyderabad, India)
Copyright: 2024
Pages: 13
Source title:
Enhancing Medical Imaging with Emerging Technologies
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Shobhit Tyagi (Sharda University, India), Prashant Upadhyay (Sharda University, India)and Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/979-8-3693-5261-8.ch012
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Abstract
In the medical domain, multimodal image fusion has emerged as a powerful technique aiming to enhance diagnostic accuracy and clinical decision-making. Image fusion technique combines two or more images from the different imaging modalities to enhance image detail and preserve information. However, single modality imaging fails to provide accurate information necessary for precise analysis and diagnosis. This chapter introduces a flask-based application that integrates multiple medical images, merging brain CT scans and MRI through landmark-based image registration. Then wavelet transform-based fusion techniques combine the registered images, providing a comprehensive view of the brain's neural structure and functions. A CNN model is then employed to identify brain tumors in the fused multimodal images. Following tumor detection, the model categorizes tumors as glioma, meningioma, or pituitary tumor. Through the incorporation of these methodologies, the application supports medical imaging and diagnosis by enhancing accuracy, efficiency, and clinical outcomes.
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