Abstract
The field of image processing is distinguished by the variety of functions it offers and the wide range of applications it has in biomedical imaging. It becomes a difficult and time-consuming process for radiologists to do the manual identification and categorization of the tumour. It is a complex and time-consuming procedure conducted by radiologists or clinical professionals to remove the contaminated tumour region from magnetic resonance (MR) pictures. It is the goal of this study to improve the performance and reduce the complexity of the image segmentation process by investigating FCM predicted image segmentation procedures in order to reduce the intricacy of the process. Furthermore, relevant characteristics are collected from each segmented tissue and aligned as input to the classifiers for autonomous identification and relegation of encephalon cancers in order to increase the accuracy and quality rate of the neural network classifier. An evaluation, validation, and presentation of the experimental performance of the suggested approach have been completed. A unique APSO (accelerated particle swarm optimization) based artificial neural network model (ANNM) for the relegation of benign and malignant tumours is presented in this study effort, which allows for the automated identification and categorization of brain tumours. Using APSO training to improve the suggested ANNM model parameters would give a unique method to alleviate the stressful work of radiologists performing manual identification of encephalon cancers from MR images. The use of an APSO-based ANNM (artificial neural network model) model for automated brain tumour classification has been presented in order to demonstrate the resilience of the classification model. It has been suggested to utilise the improved enhanced fuzzy c means (IEnFCM) method for image segmentation, while the GLCM (gray level co-occurrence matrix) feature extraction approach has been employed for feature extraction from magnetic resonance imaging (MR pictures).
Highlights
In the current medical sciences and clinical research developments, many models have been developed for earlier tumor detection for saving a patient’s life
As a result of this, the primary goal of this study is to build a soft computing technique-based brain tumour diagnostic model, known as APSO based artificial neural network model (ANNM), in which preprocessing of magnetic resonance imaging (MRI) images is accomplished by the use of a median filter
Each training and testing phase is processed with 18 brain samples divided into 9 each for training and testing, respectively. e trained representatives are provided as input to the ANN for training, and the validation is carried out with the testing samples
Summary
In the current medical sciences and clinical research developments, many models have been developed for earlier tumor detection for saving a patient’s life. An automated and efficient model is required for handling the complexities and time consumption in tumor image diagnosis from MRI scans [4] It is observed from several types of research that soft computing techniques are effectively incorporated for processing. As a result of this, the primary goal of this study is to build a soft computing technique-based brain tumour diagnostic model, known as APSO (accelerated particle swarm optimization) based artificial neural network model (ANNM), in which preprocessing of MRI images is accomplished by the use of a median filter.
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