To Detect and Classify Brain Tumors using CNN and ANN as an asset of Deep Learning and to examine the position of the tumor.

This repository is part of the Brain Tumor Classification Project. The repo contains the Flask app for the project.

A comparative analysis of brain tumor classification and detection using machine learning and deep learning algorithms

Developed a Brain Tumor Detection system using machine learning and image processing techniques to classify MRI scans. The model leverages deep learning algorithms (CNNs) for accurate tumor identification, aiding early diagnosis and medical decision-making. Implemented data preprocessing, feature extraction, and classification to enhance detection

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A deep learning project that classifies brain tumors from MRI images into four categories—glioma, meningioma, pituitary tumor, and no tumor—using Convolutional Neural Networks (CNNs) for accurate and automated diagnosis support.

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Brain tumor classification using deep learning algorithm in django website

Brain tumor classification involves using machine learning or deep learning algorithms to analyze medical images (like MRIs) and categorize brain tumors into different types, such as benign or malignant

"Innovative Brain Tumor Classification 🧠🔍 🚀 Developed a robust deep learning model for accurate brain tumor classification using MRI datasets. Leveraging cutting-edge deep learning algorithms, this project represents a significant stride in medical diagnostics.

Detecting and Separating Brain Tumor using, CNN and automated classification techniques using Machine Learning. Therefore, developed a system that enables detection and classification using Deep Learning algorithms.

Brain Tumor detection is an important deep learning technique which uses different machine learning and deep learning algorithms that are are used for classification and segmentation.

Brain Tumor Detection and Classification involves using medical imaging techniques—primarily MRI scans—along with machine learning or deep learning algorithms to identify the presence of a tumor a tumor and determine its type.

Brain tumor detection involves the identification and classification of abnormal growths in the brain using medical imaging techniques such as MRI (Magnetic Resonance Imaging) and CT (Computed Tomography) scans.This process leverages advanced image processing,deep learning & machine learning algorithms to analyze & detect tumors with high accuracy.

This repository investigates brain tumor classification using MRI data. It compares deep learning models (CNN, ResNet) with machine learning algorithms (GBM, RF, KNN, LR, SVM). The study shows deep learning models outperforming traditional methods, highlighting their potential in enhancing diagnosis accuracy and patient care.

A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties. Application of automated classification techniques using Machine Learning(ML) and Artificial Intelligence(AI)has consistently shown higher accuracy than manual classification. Hence, proposing a system performing detection and classification by using Deep Learning Algorithms using Convolution Neural Network (CNN), Artificial Neural Network (ANN), and Transfer Learning (TL) would be helpful to doctors all around the world.

A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties. Application of automated classification techniques using Machine Learning(ML) and Artificial Intelligence(AI)has consistently shown higher accuracy than manual classification. Hence, proposing a system performing detection and classification by using Deep Learning Algorithms using ConvolutionNeural Network (CNN), Artificial Neural Network (ANN), and TransferLearning (TL) would be helpful to doctors all around the world.

Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2 million new cases in 2018, making it a significant health problem in present days. The key challenge in breast cancer detection is to classify tumors as malignant or benign. Malignant refers to cancer cells that can invade and kill nearby tissue and spread to other parts of your body. Unlike cancerous tumor(malignant), Benign does not spread to other parts of the body and is safe somehow. Deep neural network techniques can be used to improve the accuracy of early diagnosis significantly. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called an artificial neural network. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms.