Automatic Detection of Brain Tumors Using Genetic Algorithms With Multiple Stages in Magnetic Resonance Images

• Published in: Journal of automation, mobile robotics & intelligent systems mobile robotics & intelligent systems, 2022-12, Vol.16 (4), p.36-43
• Main Authors: Annam, Karthik, Kumar, G Sunil, Babu, P Ashok, Domala, Narsaiah
• Format: Article
• Language: English
• Subjects: advanced machine learning; genetic optimization; GLCM; MRI brain tumor; SURF
• ISSN: 2080-2145; 2080-2145
• DOI: 10.14313/jamris/4-2022/31

The field of biomedicine is still working on a solution to the challenge of diagnosing brain tumors, which is now one of the most significant challenges facing the profession. The possibility of an early diagnosis of brain cancer depends on the development of new technologies or instruments. Automated processes can be made possible thanks to the classification of different types of brain tumors by utilizing patented brain images. In addition, the proposed novel approach may be used to differentiate between different types of brain disorders and tumors, such as those that affect the brain. The input image must first undergo pre-processing before the tumor and other brain regions can be separated. Following this step, the images are separated into their respective colors and levels, and then the Gray Level Co-Occurrence and SURF extraction methods are used to determine which aspects of the photographs contain the most significant information. Through the use of genetic optimization, the recovered features are reduced in size. The cut-down features are utilized in conjunction with an advanced learning approach for the purposes of training and evaluating the tumor categorization. Alongside the conventional approach, the accuracy, inaccuracy, sensitivity, and specificity of the methodology under consideration are all assessed. The approach offers an accuracy rate greater than 90%, with an error rate of less than 2% for every kind of cancer. Last but not least, the specificity and sensitivity of each kind are higher than 90% and 50%, respectively. The usage of a genetic algorithm to support the approach is more efficient than using the other ways since the method that the genetic algorithm utilizes has greater accuracy as well as higher specificity.