A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers
Volume-2 | Issue-3
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach
Volume-3 | Issue-3
Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme
Volume-2 | Issue-3
Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks
Volume-3 | Issue-3
Computer Vision on IOT Based Patient Preference Management System
Volume-2 | Issue-2
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Volume-3 | Issue-4
Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security
Volume-4 | Issue-3
Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network
Volume-3 | Issue-2
Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Volume-3 | Issue-3
Future Challenges of the Internet of Things in the Health Care Domain - An Overview
Volume-3 | Issue-4
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
Volume - 5 | Issue - 3 | september 2023
Published
17 July, 2023
Brain tumors are one of the more severe medical conditions that can affect both children and adults. Brain tumors make up between 85 and 90 percent of all primary Central Nervous System (CNS) malignancies. Each year, brain tumors are found in about 11,700 persons. The 5-year survival rate is around 34% for males and 36% for female patients with malignant brain or CNS tumors. Brain tumors can be classified as benign, malignant, pituitary, and other forms. Appropriate treatment, meticulous planning, and exact diagnostics must be used to prolong patient lives. The most reliable way for detecting brain cancer is Magnetic Resonance Imaging (MRI). The images are examined by the radiologist. As brain tumors are complex the MRI serve as guide to diagnose the seriousness of the disease. Since the placement and size of the brain tumor seems incredibly abnormal for persons affected by the disease it becomes difficult to properly comprehend the nature of the tumor. For MRI analysis, a qualified neurosurgeon is also necessary. Compiling the results of an MRI can be extremely difficult and time-consuming because there are typically not enough qualified medical professionals and individuals who are knowledgeable about malignancy in poor countries. Thus, this issue can be resolved by an automated cloud-based solution. In the proposed model, The Convolutional Neural Networks (CNN) is used for the classification of the brain tumor dataset with an accuracy of 99%.
KeywordsMRI (Magnetic Resonance Imaging) Brain tumor Transfer Learning Convolution Neural Network (CNN) Image Processing
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