Abstract

Brain tumour to be effectively treated, it is essential to identify and diagnose it.Medical image processing has advanced tremendously in recent years due to machine techniques for learning. Convolutional neural networks (CNNs) are deep learning models that have shown remarkable performance in several health care applications, such as the identification of brain tumours that are malignant. CNNs are a useful tool for clinical image analysis because they are capable of extracting pertinent features from enormous datasets without the necessity of feature engineering But problems including the absence of standardised datasets, the requirement for substantial volumes of annotations, and the comprehensibility of the models continue to be issues. Despite these difficulties, machine learning algorithms have tremendous possibilities for healthcare picture interpretation, and recent developments like explainable artificial intelligence, federated training, and transferred learning show promise for overcoming these restrictions. Additionally, new potential for machine learning algorithms to enhance brain tumour detection and diagnosis have been created by developments in imaging technologies including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). A patient's health can be shown in greater detail by the combination of digital health records and other sources of healthcare data, which can help healthcare providers make better treatment decisions. In conclusion, the identification and evaluation of brain tumours have demonstrated encouraging results when using machine learning algorithms, notably CNNs. However, further research is necessary to address the current challenges and limitations and to fully realise the potential of this technology in improving patient outcomes.