Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images

Abstract

The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.

Mind Control Robotic Arm: Augmentative and Alternative Communication in the Classroom Environment

“…Fortunately, researchers at the University of Minnesota have devised an alternative: a system that allows humans to move a robotic appendage through the sole use of their thoughts, and which does not require surgery or brain implants. It's a significant step forward in the development of non-invasive brain-computer interfaces (BCIs), which establish direct contact between the brain and an external device [4,5]. The purpose of the paper is to create a robot arm that could assist people with disabilities in their daily lives and make their work independent of others.…”

“…BCIs have the capability to monitor brain activity, interpret the acquired data, and convert this into output that can improve human activities [16]. The potential applications span from restoring lost functions like speech and voluntary move-ment, to enhancing neurological pathways for skills like grasping, to aiding as additional limbs [5]. The paper discusses different methods of assessing brain activity with BCIs, en-compassing invasive techniques like cortical electrode insertion to noninvasive techniques like scalp electroencephalography (EEG) electrodes.…”

Deep convolutional neural network algorithm for the automatic segmentation of oral potentially malignant disorders and oral cancers

“…An image can be classified using a single label or multiple labels, and a label can be issued based on one or more criteria. [26][27][28][29][30][31][32][33] Instead of detecting only advanced OCs, the motivation for this study is to build a deep CNN model both for the detection of OPMDs and OCs using clinical image dataset with histopathological results and raise the consciousness of both researchers and clinicians about the possible challenges of developing that CNN model.…”

“…An image can be classified using a single label or multiple labels, and a label can be issued based on one or more criteria. 26–33…”