Abstract
Recent strides in artificial intelligence (AI) and deep learning techniques have propelled the development of an AI-powered brain tumour detection model. This study blends multilevel thresholding, neural network optimisation, and image preprocessing to craft a robust AI model capable of accurately categorising diverse brain tumour types and normal cases. Through rigorous testing with a comprehensive dataset of 1747 images, the model achieves an accuracy of 92%. Its integration into a user-friendly smartphone app, MediScan, enhances accessibility and practicality. The app provides heatmap visualisations and generates diagnostic reports, supporting medical professionals in making swift decisions. The model prioritises interpretability enhancement and has the potential to cultivate collaboration between AI experts and medical practitioners, thus advancing the field of brain tumour detection and diagnosis. While promising, the model demands computational resources and diverse datasets. This research also highlights AI's potential to transform healthcare diagnostics, ensuring precise and efficient brain tumour identification.
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