TY - JOUR AU - Ruchita Rathod, Dr. Rais Abdul Hamid Khan, PY - 2021/05/12 Y2 - 2024/03/28 TI - BRAIN TUMOR DETECTION USING DEEP NEURAL NETWORK AND MACHINE LEARNING ALGORITHM JF - PalArch's Journal of Archaeology of Egypt / Egyptology JA - J Arch.Egyptol VL - 18 IS - 08 SE - DO - UR - https://www.archives.palarch.nl/index.php/jae/article/view/8814 SP - 1085-1093 AB - <p>The determination of tumor extent&nbsp;may be a&nbsp;major challenging task in&nbsp;brain tumor&nbsp;planning and quantitative evaluation. Magnetic Resonance Imaging (MRI) is&nbsp;one among&nbsp;the non-invasive techniques that has emanated as a front- line diagnostic tool for&nbsp;brain tumor&nbsp;without&nbsp;radiation. Deep learning has shown remarkable progress in image-recognition jobs. Works on going from convolutional neural networks (CNN) to variational auto encoders have discovered endless applications in the medical picture investigation field, driving it forward at a fast speed. In radiology, the experienced doctor outwardly assessed clinical pictures for the recognition, portrayal, and observing of illnesses. In this work, automatic brain tumor detection is proposed by using Machine learning and Convolutional Neural Networks (CNN) classification. The deeper architecture design is performed by small kernels. The neuron’s weight is given as small. It is observed that CNN achieves a good rate of accuracy with low complexity as compared to all other&nbsp; methods. This improved accuracy will help doctors to treat well.</p><p>&nbsp;</p> ER -