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@article{174643,
        author = {Deepak Kumar and Dr Aakriti Jain and Dr Sitesh kumar Sinha},
        title = {The study of Brain Tumor Detection using Image Classification and machine Learning: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {350-358},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174643},
        abstract = {Brain tumors are abnormal cells in the brain and can be benign or malignant. Early detection and accurate diagnosis are important for optimal treatment and better patient outcomes. Traditional diagnostic procedures rely heavily on the interpretation of medical images; This can be time consuming and prone to human error. Machine learning (ML) and image classification techniques show promise in automating and improving the accuracy of brain diagnosis. Brain cancer is one of the most common and life-threatening diseases affecting the central nervous system. They can be benign (non-cancerous) or malignant (cancerous); the latter pose a serious risk to life due to their aggressive nature. Early detection and accurate diagnosis are important for optimal treatment and better patient outcomes. Traditional medical examinations often rely on a dictionary of medical images that are time-consuming, subject to review by different examiners, and prone to human error. It is revolutionizing many fields, including medical imaging. Machine learning, especially deep learning techniques, holds promise for improving the processing and accuracy of mental health diagnoses. By training algorithms on large amounts of medical data, these machines can learn to recognize complex and unusual patterns that may indicate tumors. Learning will cover various machine learning models, types of clinical data used, measurement methods, and challenges faced in the field. Through this review, we aim to provide an overview of how machine learning can be used in brain cancer diagnosis, highlighting its potential and potential challenges that need to be overcome for clinical success.},
        keywords = {Machine Learning (ML), Magnetic Resonance Imaging (MRI), Diffusion-weighted imaging (DWI), Support Vector Machines(SVM), Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs)},
        month = {March},
        }