Interpretative Artificial Intelligence for Brain Tumor Detection and Classification
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Abstract
A brain tumor, a predominant source of unregulated cellular proliferation in the central nervous system, poses significant difficulties in medical detection and therapy. Timely and precise identification is crucial for successful intervention. Early malignancies within adult brains are universally lethal. Recent advancements in artificial intelligence (AI) within the realm of computer vision have facilitated the automated characterization and diagnosis of brain tumor lesions. The precise identification and categorization of brain tumors are essential elements of medical diagnosis. Recently, AI methodologies have gained prominence in improving brain tumor detection. Nonetheless, AI models frequently exhibit a deficiency in transparency, which poses significant challenges in critical domains such as healthcare. This study presents an Explainable AI (XAI) system designed for brain tumor identification, providing doctors with clear and interpretable insights into model decisions. This system utilizes advanced XAI techniques to enhance confidence, reliability, and clinical acceptance of AI-based tumor detection technologies. Creating explainable AI methodologies will be crucial for enhancing human-machine interactions and aiding in the identification of appropriate training techniques. Future endeavors will enhance the dataset and implement discoveries in real-time diagnostic equipment, therefore improving the discipline.
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