Authors: Mr. Mallikarjun Birajdar, Mr. Abhishek Nandgaonkar , Mr. Rupesh Patil, Mrs. Anushree Mutalik, Dr. Deepali Nikam, Prof. S.A. Narde
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Brain tumors are a major global health issue, and successful treatment frequently depends on an early and precise diagnosis. Traditional methods of brain tumor detection, such as manual interpretation of medical images, can be time-consuming and prone to human error. Machine learning techniques have emerged as a promising approach to assist medical professionals in the early detection and classification of brain tumors. This study presents a novel method for brain tumor detection utilizing machine learning algorithms .The dataset used in this research comprises a collection of brain MRI (Magnetic Resonance Imaging)scans from diverse sources, including both tumor and non-tumor cases. We preprocess the data by enhancing image quality and for classification, different machine learning techniques are used, such as random forests, support vector machines (SVMs), and convolutional neural networks (CNNs).
Primary brain tumors, which encompass both benign and malignant tumors, are a leading cause of morbidity and mortality in oncological patients, leading to disabilities and encumbering families as well as the health care system. A framework that capitalizes on automatic segmentation of brain tumors using MRI may increase diagnostic accuracy, and deliver a classification within a short time frame. The focus of the present study is, as a result, automatic segmentation of brain tumors in MRI using multi-scale deep versus convolution neural network with small convolution kernels. The most common type of tumors in the human brain is the gliomas tumors. Gliomas are divided into two main groups based on their cellular features: low-grade glioma (LGG) that is considered as benign and high-grade glioma (HGG) that is considered as malignant. Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form anatomical images and the physiological processes of the body. In order to produce images of the organs in the body, MRI scanners employ magnetic field gradients, strong magnetic fields, and radio waves. MRI is a non-intrusive system that does not include X-ray and does not use of ionizing radiation. These features distinguish MRI from other imaging techniques, such as computed tomography (CT) and Positron emission tomography (PET) scans. Brain tumors are a significant and life-threatening medical condition, with early and accurate diagnosis playing a crucial role in effective treatment and patient outcomes. The conventional methods of brain tumor detection, relying on manual interpretation of medical images such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, are not only time-consuming but are also susceptible to human error. In recent years, the integration of machine learning techniques into medical imaging has shown great promise in improving the efficiency and accuracy of brain tumor detection. Machine learning, a subset of artificial intelligence, empowers computers to learn patterns and make data-driven predictions from large datasets. In the context of brain tumor detection, machine learning algorithms can analyze complex medical images, identify subtle abnormalities, and assist medical professionals in making informed decisions. This paper explores the application of machine learning in brain tumor detection, aiming to highlight its potential in revolutionizing the field of neuroimaging.
II. LITERATURE REVIEW
One of the most challenging as well as demanding task is to segment the region of interest from an object and segmenting the tumor from an MRI Brain image is an ambitious one. Researchers around the world are working on this field to get the best-segmented ROI and various disparate approaches simulated from a distinct perspective. Nowadays Neural Network based segmentation gives prominent outcomes, andthe flow of employing this model is augmenting day by day.
Yantao et al.  resembled Histogram based segmentation technique. Regarding the brain tumor segmentation task as a three-class Interpretability of machine learning models in medical applications is essential for gaining the trust of healthcare professionals.
Researchers have developed methods to visualize and explain the decision-making processes of these models, providing insights into the features contributing to tumor detection. Tissue) classification problem regarding two modalities FLAIR and T1.
The abnormal regions were detected by using a regionbased active contour model on FLAIR modality. The edema and tumor tissues were distinguished in the abnormal regions based on the contrast enhancement T1 modality by the k-means method and accomplished a Dice coefficient and sensitivity of 73.6% and 90.3% respectively.
Devkota et al.  established the whole segmentation process based on Mathematical Morphological Operations and spatial FCM algorithm which improves the computation time, but the proposed solution has not been tested up to the evaluation stage and outcomes as- Detects cancer with 92% and classifier has an accuracy of 86.6%.
In , Brain tumor detection and removal have been suggested using a Fuzzy C-Means clustering technique, conventional classification algorithms, as well as a CNN to process 2D MRIs of the brain. Experiments were conducted using a real-time dataset consisting of tumor images of a variety of intensities, dimensions,
Pei et al.  proposed a technique which utilizes tumor growth patterns as novel features to improve texture based tumor segmentation in longitudinal MRI. Label maps are being used to obtain tumor growth modeling and predict cell density after extracting textures (e.g., fractal, and mBm) and intensity features. Performance of the model reflected as the Mean DSC with tumor cell density.
III. EXISTING SYSTEM
Detecting brain tumors using machine learning is a critical application in the field of medical imaging and healthcare. Several existing systems and approaches have been developed to aid in brain tumor detection and diagnosis. Here are some key aspects of the existing systems for brain tumor detection using machine learning:
IV. PROPOSE SYSTEM
In our proposed work, the purpose of our proposed model is to build upon the current CNN-based image classification method, which includes Initialize GUI, segmentation, feature extraction and classification of MRI images, by correcting for its limitations: potential for computational load due to separate segmentation of normal brainimage and tumor brain image [I], and potential for errors in classification due to pooling of image features.
The framework, or skeleton, of our proposed model uses the steps and features of the current state-of-the-art model as its basis, but we implemented a DNNmodel based on an enhanced Conditional Random Field (CRF) algorithm with the aim of overcoming the slowness, and improving the precision, of brain tumor segmentation from MRI images as compared with the current state-ofthe-art method.
In the end, our aim was to develop an automated brain tumor segmentation framework that makes easier the early diagnosis of brain tumors using MRI for medical personnel, enabling early intervention and follow-up to reduce mortality.
V. FUTURE WORK
This describes the execution of the proposed system in the detection of various diseases using CNN. The entire architecture depicts how the system deals with the recognition and detection of the test image, and below we explain the process of execution. The purpose of this research is to combine feature selection approaches with machine learning to identify pre-illnesses. For the early diagnosis of early diseases in MRI, CT scan, and X-ray images, this system makes use of deep learning techniques and image processing technology.
To make feature extraction more efficient, the dataset including defective images from several categories was preprocessed and segmented. Image Acquisition: In image acquisition, heterogeneous images of the medical dataset collected which contain subnormal and normal samples are gathered from a variety of individuals and converted into image format using a camera or some synthetic dataset.
This project aims to leverage CNNs to improve the accuracy and efficiency of brain lesion detection from MRI images. By exploring the capabilities of deep learning in medical image analysis, this work contributes to advancements in automated diagnostic tools for healthcare professionals.
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Copyright © 2023 Mr. Mallikarjun Birajdar, Mr. Abhishek Nandgaonkar , Mr. Rupesh Patil, Mrs. Anushree Mutalik, Dr. Deepali Nikam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.