Shaik Karimullah,
Ali H.Wheeb,
Fahimuddin Shaik,
- Associate Professor Department of Electronics & Communication Engineering ,Annamacharya Institute Of Technology & Sciences, Rajampet Andhra Pradesh India
- Associate Professor Department of Computer Engineering, University of Baghdad Baghdad Iraq
- Associate Professor Department of Electronics & Communication Engineering ,Annamacharya Institute of Technology & Sciences, Rajampet Andhra Pradesh India
Abstract
Primary brain tumour detection and classification are critical factors in ensuring effective treatment and, ultimately, improving patient well-being. This paper describes a novel method for detecting and classifying brain tumours with the help of MRI and CT images. The suggested method combines Harmony Search Optimisation (HSO) and Convolution Neural Networks (CNN) based on Deep Learning techniques, yielding an impressive accuracy rate of 99.13% for both detection and classification tasks. Furthermore, the system has exceptional specificity (99.2243%) and sensitivity (99.245%), highlighting its precision in distinguishing true negatives and true positives. This incorporation of advanced methodologies not only improves diagnostic accuracy but also reduces reliance on radiologists’ expertise. The experimental results support the proposed approach’s effectiveness, demonstrating its ability to detect and classify brain tumours from medical images. This breakthrough is a noteworthy step forward in the fields of medical image analysis and brain tumour diagnosis, paving the way for better patient care and treatment outcomes.
Keywords: MRI and CT images, Convolutional Neural Network (CNN), Harmony Search Optimization
[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]
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Journal of Artificial Intelligence Research & Advances
Volume | 11 |
Issue | 03 |
Received | August 21, 2024 |
Accepted | September 2, 2024 |
Published | September 5, 2024 |