Brain tumor detection model based on CNN and threshold segmentation
Journal Title: International Journal of Experimental Research and Review - Year 2023, Vol 32, Issue 3
Abstract
Brain tumours pose a substantial global health issue, emphasising the criticality of timely and precise identification to ensure optimal treatment outcomes. This research introduces an innovative methodology for the identification of brain tumours by employing a fusion of Convolutional Neural Networks (CNNs) with threshold segmentation techniques. The objective of the suggested model is to improve the precision and effectiveness of brain tumour identification in medical imaging, namely Magnetic Resonance Imaging (MRI) scans. Fast and precise diagnosis is necessary in the medical profession for effective treatment, but current technologies lack this capability. For successful therapy, it is therefore necessary to develop an effective diagnosis application. Global threshold segmentation for pre-processing is used in this study. Image capture and de-noising were completed in the first stage, while classification and regression were completed in the second stage using ML approaches. A computer-aided automated identification method is the computational technique used in this study. This investigation uses one hundred twenty (120) brain scans from a real-time MRI brain database, of them 15 normal and 105 abnormal. According to performance metrics, the accuracy of training and testing pictures was 99.46%. Comparing this method to recently published methods, it is determined that LR-ML with the threshold segmentation has a rapid, precise brain diagnostic system.
Authors and Affiliations
Jaishree Jain, Shashank Sahu, Ashish Dixit
Keywords
Related Articles
- EP ID EP720681
- DOI 10.52756/ijerr.2023.v32.031
- Views 22
- Downloads 0
Metal contamination in traditionally used Medicinal plants: a serious threat in Murshidabad district, West Bengal, India
Murshidabad district is one of the most highly Arsenic (As) prone areas of West Bengal, India. The predominantly rural population of this district greatly depends on traditionally used medicinal plants for treatment of v...
Breast Cancer Disease Prediction Using Random Forest Regression and Gradient Boosting Regression
The current research endeavors to evaluate the efficacy of regression-based machine learning algorithms through an assessment of their performance using diverse metrics. The focus of our study involves the implementation...
Assessing diversity of Maize (Zea mays L.) genotypes based on multivariate analysis of the quantitative traits
Fourteen genotypes along with one standard check of maize were evaluated in Randomized Complete Block Design with three replications. Observations were taken for days to 50% germination, 50% tasselling, silking, tasselli...
The Relationship between Core Muscle Stability and Balance in Yoga
Core muscle stability includes the strength and endurance of the flexor and extensor muscles present at deep inside of the trunk region of our body which is considered as an important factor for sports performance. Purpo...
Early Blight and Late Blight Disease Detection in Potato Using Efficientnetb0
Potatoes are an important crop heavily consumed by Indian food products. It is produced on a massive scale, with China, India, Russia, Poland, and the USA being the main producers. Numerous leaf diseases harm the crop du...