Ai-Driven Healthcare: Enhancing Diagnosis and Treatment Via Fuzzy Convolutional Neural Networks

Abstract

Artificial Intelligence (AI) is catalysing a dramatic shift in healthcare by integrating advanced technology with patient-focused solutions, hence facilitating AI-Driven Healthcare. This transition addresses the increasing demand for improved efficiency, diagnostic accuracy, and individualized care. Artificial intelligence is progressively acknowledged as a pivotal instrument for transforming patient management, encompassing diagnostics, therapy planning, and administrative functions. This research introduces an advanced, computer-assisted framework utilizing a FPCNN to facilitate AI-Driven Healthcare. The model entails preprocessing unrefined healthcare data, identifying ideal features with the EOSSA, and categorizing results utilizing machine learning methodologies. The FPCNN model, engineered for medical image classification, categorizes data into normal, COVID, or pneumonia classifications. The application of fuzzy pooling across all layers enables the model to attain an exceptional classification accuracy of 98.17%, surpassing current methodologies. The inclusion of interpretable decision explanations guarantees transparency and equity in AI-driven diagnostics. These findings underscore the potential of FPCNN as a formidable instrument in clinical environments, bolstering precision medicine and augmenting confidence in AI applications. This research highlights the essential role of AIDriven Healthcare in enhancing patient outcomes and influencing the future of medical decision-making.

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